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Multi-objective optimization of reservoir refill operate considering energy storage and hydrological conditions 认领 引用
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作者 Lihua Chen Kaipeng Yang +2 位作者 Yunyao Chen Xiaoli Zhang Xuefang Li 《River》 EI CSCD 2026年第2期282-294,共13页
Optimizing reservoir refill operation rules is crucial for enhancing reservoir sustainability and resilience.This study proposes a refill operation model designed to derive optimal refill guide curves by considering f... Optimizing reservoir refill operation rules is crucial for enhancing reservoir sustainability and resilience.This study proposes a refill operation model designed to derive optimal refill guide curves by considering flood risk prevention,maximum storage levels,and the combined benefits of hydropower generation and energy storage.The proposed model was applied to the Longtan Reservoir in China.The results indicate that employing various optimal refill guide curves,tailored to wet,normal,and dry hydrological conditions,yields better outcomes than using a single refill guide curve.In wet years,the maximum annual combined benefits of hydropower generation and energy storage increased by 2.88%.In normal and dry years,the average annual water level at the end of the water storage period was significantly raised by 0.71 and 1.9 m,respectively.Correspondingly,the reservoir storage volume increased by 2.53×108m3and 6.19×108m3,respectively.These findings demonstrate that the proposed approach can enhance the benefits of refill operations at the Longtan Reservoir under varying hydrological conditions. 展开更多
关键词 energy storage hydrological conditions multi‐objective optimization NSGA‐II reservoir refill guide curves
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An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization(MCCMO)for Multi-Objective Optimization Problem 认领 引用
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作者 Muhammad Waqar Khan Adnan Ahmed Siddiqui Syed Sajjad Hussain Rizvi 《Computers, Materials & Continua》 SCIE EI 2026年第2期1874-1888,共15页
The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant... The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability. 展开更多
关键词 MCCMO algorithms adaptive diversity preservation RWMOP50 power distribution system multi-modal multi objective optimization evolutionary algorithm multi objective problem
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An efficient multi‑objective optimization framework based on data‑driven identification and adaptive directed correction for complex distillation processes 认领 引用
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作者 Fucheng Xu Lu Yang +6 位作者 Zihao Wang Tao Shi Rongsheng Lin Bohong Wang Hengcong Tao Zhiliang Cheng Weifeng Shen 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第7期25-35,共11页
Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient explorat... Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient exploration of feasible,high‑performance regions of the search space.In this study,we propose a data‑driven identification and adaptive directed correction strategy for handling infeasible solutions,and on this basis,develop an efficient multi‑objective optimization framework(MO‑DIDC)for complex distillation processes.By identifying infeasible solutions that closely resemble high‑performance ones,the framework leverages them to accelerate convergence to optimal designs.A surrogate model is trained to distinguish high‑and low‑performance solutions and is then used to identify potentially high‑performance candidates within the infeasible set.Through similarity analysis,the most influential variable is selected for correction to generate new promising solutions.This strategy reduces unnecessary exploration of infeasible regions and concentrates computational effort on feasible,high‑quality solutions.Demonstrated on a side‑stream double‑column extractive distillation system and a four‑column extractive distillation system,the proposed optimization framework outperforms a widely used genetic algorithm while substantially improving computational efficiency,achieving optimization time reductions of 35.3%and 20.8%,respectively.Overall,the proposed MO‑DIDC framework provides an effective and computationally efficient tool for the optimization of complex distillation processes. 展开更多
关键词 data‑driven identification adaptive directed correction infeasible solutions handling multi‑objective optimization complex distillation process
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Multi-objective ANN-driven genetic algorithm optimization of energy efficiency measures in an NZEB multi-family house building in Greece 认领 引用
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《建筑节能（中英文）》 CAS 2026年第2期62-62,共1页
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu... The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%. 展开更多
关键词 energy efficiency measures gas boilerssplit units building envelope components energy efficiency economic performance artificial neural network ann driven multi objective optimization economic performance optimization ANN driven GA methods
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Multi-objective optimization of grinding process parameters for improving gear machining precision 认领 引用 被引量：4
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作者 YOU Tong-fei HAN Jiang +4 位作者 TIAN Xiao-qing TANG Jian-ping LU Yi-guo LI Guang-hui XIA Lian 《Journal of Central South University》 SCIE EI CAS CSCD 2025年第2期538-551,共14页
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus... The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods. 展开更多
关键词 worm wheel gear grinding machine gear machining precision machining process parameters multi objective optimization
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MSA-DETR:Multi-scale attention enhanced DETR for object detection in oilfield surveillance 认领 引用
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作者 Qian-Wen Cao Jin-Rong Ma Lai-Bin Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2686-2697,共12页
In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervisio... In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervision of personnel and equipment.However,safety-critical targets in drilling scenes are often small,partially occluded,and embedded in cluttered environments,leading to decreased detection accuracy and potential safety risks.Existing convolutional neural networks(CNN)-based detectors,although effective in natural scenes,often exhibit limited robustness under such complex industrial conditions.To address these challenges,this paper proposes MSA-DETR,a Transformer-based detection framework designed to enhance multi-scale perception in drilling monitoring scenarios.By improving the ability to capture both global contextualinformation andfine-grained visual cues,the proposed approach enhances sensitivity to safety-relevant objects.Extensive experiments conducted on two realworld drilling monitoring datasets demonstrate that MSA-DETR consistently outperforms state-of-theart detection methods,providing more reliable visual perception for petroleum safety management and accident prevention. 展开更多
关键词 Object detection Petroleum drilling safety Multi-scale perception Drilling engineering Artificial intelligence
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Railway Track Defect Detection Based on Dynamic Multi-Modal Fusion and Challenging Object Enhanced Perception 认领 引用
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作者 Yaguan Wang Linlin Kou +3 位作者 Yang Gao Qiang Sun Yong Qin Genwang Peng 《Structural Durability & Health Monitoring》 EI 2026年第2期195-212,共18页
The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defec... The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset. 展开更多
关键词 Railway safety track defect detection multi-modal data object detection
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An end-to-end object detection method for autonomous driving in multiple adverse weather conditions 认领 引用
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作者 YIN Shi LIU Hui 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第5期2406-2422,共17页
Autonomous driving systems impose stringent requirements on the real-time performance and computational efficiency of visual perception tasks,particularly under complex and diverse adverse weather conditions.To addres... Autonomous driving systems impose stringent requirements on the real-time performance and computational efficiency of visual perception tasks,particularly under complex and diverse adverse weather conditions.To address these challenges,a highly robust object detection method called Adverse-Det is proposed,targeting multiple harsh weather scenarios.This model introduces the visual state-space modeling module and the frequency-aware feature fusion module to achieve dual enhancement in global spatial structure modeling and local detail recovery,effectively mitigating the performance degradation caused by image quality deterioration in challenging environments such as rain,fog,sandstorms,and snow.Experimental results on the public DAWN dataset demonstrate that Adverse-Det achieves high detection accuracy across various scenes and weather conditions.Compared with baseline models,Adverse-Det improves the mean Average Precision at intersection over union thresholds from 0.5 to 0.95(mAP50:95)by an average of 18.7%,achieving an mAP50:95of 0.455 under snowy conditions.In addition,on the self-constructed real-world rainy weather driving dataset Rain-Drive,Adverse-Det achieves a 4.56%improvement in mAP50:95.These results fully verify the effectiveness and strong generalization capability of the proposed method in complex real-world weather environments,providing solid technical support for the safe and reliable operation of autonomous driving systems under adverse weather conditions. 展开更多
关键词 object detection autonomous driving adverse weather
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Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images 认领 引用
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作者 Shuaiyu Zhu Sergey Ablameyko Ji Li 《Computers, Materials & Continua》 SCIE EI 2026年第8期1382-1398,共17页
Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the ... Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery,this paper proposes two improvement strategies.Method 1:(a)a Large Separable Kernel Attention(LSKA)mechanism is introduced into the backbone network to enhance feature extraction for small objects;(b)a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion,thereby improving the detection performance of objects at different scales.Method 2:(a)the Gold-YOLO structure is also integrated into the neck network;(b)a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects.To verify the effectiveness of the proposed improvements,experiments are conducted on the DOTAv1 dataset.The results show that,while maintaining the lightweight advantage of the model,the proposed methods improve detection accuracy(mAP@0.5)by 1.3%and 1.8%,respectively,compared with the baseline YOLOv11n,demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images. 展开更多
关键词 Remote sensing imagery YOLOv11n multi-scale object detection lightweight deep learning attention mechanism feature fusion
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Dimensional synchronous modeling-based enhanced Kriging algorithm and adaptive Copula method for multi-objective synthetical reliability analyses 认领 引用 被引量：1
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作者 Cheng LU Yunwen FENG +1 位作者 Chengwei FEI Da TENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第9期144-165,共22页
To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise mode... To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses. 展开更多
关键词 Adaptive Copula method Aeroengine turbine bladeddisc Aircraft landing gear system Correlation of multianalytical objectives Dimensional synchronous modeling-based enhanced Kriging algorithm Reliability analyses
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Nash Bargaining Solution-Based Multi-Objective Model Predictive Control for Constrained Interactive Robots 认领 引用
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作者 Minglei Zhu Jun Qi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1516-1518,共3页
Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained i... Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed. 展开更多
关键词 constrained interactive robots constrained interactive robotconsidering force path following interaction force modela interaction force control Nash bargaining solution path following problem multi objective model predictive control
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Face-Pedestrian Joint Feature Modeling with Cross-Category Dynamic Matching for Occlusion-Robust Multi-Object Tracking 认领 引用
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作者 Qin Hu Hongshan Kong 《Computers, Materials & Continua》 SCIE EI 2026年第1期870-900,共31页
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba... To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions. 展开更多
关键词 Cross-category dynamic binding joint feature modeling face-pedestrian association multi object tracking occlusion robustness
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Transformer-Driven Multimodal for Human-Object Detection and Recognition for Intelligent Robotic Surveillance 认领 引用
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作者 Aman Aman Ullah Yanfeng Wu +3 位作者 Shaheryar Najam Nouf Abdullah Almujally Ahmad Jalal Hui Liu 《Computers, Materials & Continua》 SCIE EI 2026年第4期1364-1383,共20页
Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To addre... Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments. 展开更多
关键词 Human object detection elderly care RGB-based pose estimation scene context analysis object recognition Gabor features point cloud reconstruction
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MKS-YOLO:A Multi-kernel Dynamic Selection Network for Object Detection in Remote Sensing Images 认领 引用
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作者 Shengfeng Guo Xiaowei He +3 位作者 Zhonglong Zheng Dawei Zhang Xuedong He Wenpeng Xia 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1918-1931,共14页
Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large co... Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large convolutional kernels or multi-scale convolutional architectures to address these challenges.However,large convolutional kernels tend to introduce substantial background noise,while the use of kernels with varying scales may lead to feature information redundancy.To tackle these issues,we propose a robust and efficient rotation-aware object detection method-MKS-YOLO.MKS-YOLO explicitly decouples high-and low-informative features through an information-aware threshold gating mechanism,effectively suppressing background noise interference.Furthermore,to reduce noise and enhance the model’s ability to perceive objects at multiple scales,we design a Multi-Kernel Dynamic Selection Module(MKSM).This module integrates multi-scale convolutional kernels and introduces a spatial attention mechanism to achieve dynamic weighted feature fusion.In addition,we incorporate a Contextual Large Kernel Attention(CLKA)mechanism to enhance feature representation capabilities for objects with high aspect ratios.Moreover,during the feature extraction stage,we introduce an Attention-based Internal Feature Interaction module(AIFI),which further strengthens the correlations among deep semantic features.To validate the effectiveness of the proposed method,we conduct extensive experiments on two widely used remote sensing image datasets:DOTA-v1.0 and DIOR-R.Experimental results demonstrate that MKS-YOLO achieves state-of-the-art detection accuracy,with Mean Average Precision(mAP)reaching 77.71%and 82.50%,respectively,fully validating the superior performance of the proposed method. 展开更多
关键词 Object detection Detectors Remote sensing Dynamic selection network
FMCSNet: Mobile Devices-Oriented Lightweight Multi-Scale Object Detection via Fast Multi-Scale Channel Shuffling Network Model 认领 引用
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作者 Lijuan Huang Xianyi Liu +1 位作者 Jinping Liu Pengfei Xu 《Computers, Materials & Continua》 SCIE EI 2026年第1期1292-1311,共20页
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio... The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection. 展开更多
关键词 Object detection lightweight network partial group convolution multilayer perceptron
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Enhanced Multi-Scale Feature Extraction Lightweight Network for Remote Sensing Object Detection 认领 引用
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作者 Xiang Luo Yuxuan Peng +2 位作者 Renghong Xie Peng Li Yuwen Qian 《Computers, Materials & Continua》 SCIE EI 2026年第3期2097-2118,共22页
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ... Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016). 展开更多
关键词 Deep learning object detection feature extraction feature fusion remote sensing
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Transformation-Equivariant Network Fused With Multi-Stage Cascade Attention for Point Cloud Object Detection 认领 引用
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作者 Jiangdong Wu Qun Chao +3 位作者 Yintai Wang Hongyuan Sun Tengfei Zhang Chengliang Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期564-577,共14页
Object detection of unmanned firefighting vehicles faces challenges such as strong electromagnetic interference,drastic lighting changes and dynamic object variations.To address these issues,we propose a two-stage 3D ... Object detection of unmanned firefighting vehicles faces challenges such as strong electromagnetic interference,drastic lighting changes and dynamic object variations.To address these issues,we propose a two-stage 3D point cloud object detection algorithm called TED-CasA-Fusion.The first stage uses the transformation-equivariant detector backbone that explicitly models rotationeflection equivariance via weight-sharing sparse convolutions,which improves detection robustness to dynamically transformed objects.The second stage introduces a cascade attention-based multistage refinement network that aggregates cross-stage object features through cascade attention modules,which effectively enhances feature representation for multiscale objects.Furthermore,the second stage integrates weighted bounding box voting to address training imbalance due to dense nearby and sparse distant point distributions,thereby improving detection accuracy for distant and sparse targets.Comparative experiments were conducted on the KITTI dataset and a self-collected firefighting dataset between the proposed algorithm and some state-of-the-art algorithms.Results show that the proposed algorithm achieves the best 3D detection accuracy for hardcategory objects on the KITTI dataset and also outperforms other detection approaches on the firefighting dataset.This work offers an efficient and reliable solution to environmental perception of unmanned firefighting vehicles. 展开更多
关键词 3D object detection cascade attention substation transformation equivariance unmanned firefighting vehicle
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MFCI-YOLO:Lightweight UAV Aerial Photography Small Object Detection Method Based on Multi-Scale Feature Fusion and Contextual Information 认领 引用
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作者 Weiguang Wang Jincai Li +6 位作者 Mengqi Liu Mengke Liu Yuan Zhang Jingyan Wu Yang Liu Junbin Lou Yixin He 《Computers, Materials & Continua》 SCIE EI 2026年第8期1962-1978,共17页
To improve the accuracy of small object feature detection in complex backgrounds for Unmanned Aerial Vehicle(UAV)aerial photography and reduce computational complexity,we propose the lightweight UAV aerial photography... To improve the accuracy of small object feature detection in complex backgrounds for Unmanned Aerial Vehicle(UAV)aerial photography and reduce computational complexity,we propose the lightweight UAV aerial photography small object detection method based on multi-scale feature fusion and contextual information.Firstly,by introducing the grouped content-aware reassembly(GCA)operator and designing lightweight pinwheel context convolution(LPConv),we extend the feature fusion path to the P2 layer,constructing a lightweight multi-scale feature fusion network(SG-PANet).Through the decoupling of fine-grained small object features and background interference features by the GCA operator,combined with the anisotropic receptive field constructed by LPConv,our proposed method can effectively preserve the geometric details of small objects.Furthermore,we introduce the cross-stage dense feature refinement(CSPStage)module as the pre-refining unit of the detection head,and use the full history state awareness mechanism to strengthen feature reuse and gradient propagation to solve the problem of feature degradation across layers.We utilize the Wise-IoU v3 loss function to dynamically optimize the gradient gains of high-quality and low-quality samples,thereby enhancing the detection accuracy and convergence speed of the proposed method in complex scenarios.Finally,we verified the superiority and generalization of the proposed method on the VisDrone2019 dataset and DOTAv1.5 dataset.The results show that compared with YOLOv11n,MFCI-YOLO’s detection mAP50-95 increased by 11.1%,small object mAP50 increased by 16.1%,and mAP50 reached 80.3%.It provides a practical solution for detecting small objects in dense scenes. 展开更多
关键词 Small object detection UAV aerial photography YOLOv11n grouped structure context-aware feature fusion lightweight
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BMGKD:A High Precision Object Detection Knowledge Distillation Method for Bridging Multi-Dimensional Gaps 认领 引用
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作者 Tianqi Wang Yang Li Zhisong Pan 《Computers, Materials & Continua》 SCIE EI 2026年第9期1862-1876,共15页
Existing knowledge distillation methods for object detection struggle to bridge the teacher-student capacity gap and overlook the inherent differences between classification and regression subtasks.To address these is... Existing knowledge distillation methods for object detection struggle to bridge the teacher-student capacity gap and overlook the inherent differences between classification and regression subtasks.To address these issues,we propose a Bridging Multi-dimensional Gaps Knowledge Distillation(BMGKD)method,which comprises two core modules:a feature difference distillation module and a response difference distillation module.The feature difference distillation module achieves global feature structural alignment via improved centered kernel alignment and performs local key feature alignment using joint spatial and channel-wise cosine similarity masks.The response difference distillation module constructs a dynamic classification mask and a high-quality prediction box selection mechanism,along with a classification and regression co-optimization loss function.On the MS COCO dataset,BMGKD achieves the highest mAP across all three teacher-student configurations on two-stage Faster R-CNN,single-stage anchor-free GFL,and single-stage anchor-based RetinaNet detectors.In the ResNet101→ResNet50 setting,it surpasses baselines by 2.7,4.2,and 2.8 percentage points,respectively,and maintains consistent optimality under varying capacity gaps and cross-architecture scenarios.On Pascal VOC 2007,BMGKD attains mAP of 56.2%,58.3%,and 58.0%on the same three detectors,outperforming all compared methods.Ablation studies confirm each component’s contribution.These results demonstrate that BMGKD effectively resolves the distillation accuracy degradation caused by capacity gaps and task discrepancies between teacher and student models.It provides an effective solution for knowledge distillation across diverse detection architectures. 展开更多
关键词 Object detection knowledge distillation model compression feature fusion
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EHDC-YOLO: Enhancing Object Detection for UAV Imagery via Multi-Scale Edge and Detail Capture 认领 引用
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作者 Zhiyong Deng Yanchen Ye Jiangling Guo 《Computers, Materials & Continua》 SCIE EI 2026年第1期1665-1682,共18页
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ... With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios. 展开更多
关键词 UAV imagery object detection multi-scale feature fusion edge enhancement detail preservation YOLO feature pyramid network attention mechanism
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