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Steel Surface Defect Detection via the Multiscale Edge Enhancement Method 认领 引用 被引量：1
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作者 Yuanyuan Wang Yemeng Zhu +2 位作者 Xiuchuan Chen Tongtong Yin Shiwei Su 《Computers, Materials & Continua》 SCIE EI 2026年第3期1006-1032,共27页
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ... To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach. 展开更多
关键词 Steel defects object detection algorithms small target multiscale attention mechanism
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Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm 认领 引用 被引量：1
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作者 WANG Baomin DING Hewei +1 位作者 TENG Fei LIU Hongqin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期309-318,共10页
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose... Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage. 展开更多
关键词 conveyer belts with steel rope cores damage X-ray image image detection improved fully convo-lutional one-stage object detection(FCOS)algorithm
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A review of deep learning-based analyses of impact crater detection on different celestial bodies 认领 引用
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作者 Xu Zhang Jialong Lai +2 位作者 Feifei Cui Chunyu Ding Zhicheng Zhong 《Astronomical Techniques and Instruments》 CSCD 2025年第3期127-147,共21页
Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying thes... Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology. 展开更多
关键词 Crater detection algorithms Deep learning Different celestial bodies Impact crater databases
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Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms 认领 引用
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作者 Liqiang Zhang Qingxia Li +7 位作者 Haitao Qiu Qingjun Zhang Yixin Gao Rong Jin Rui Wang Huan Zhang Zhongkai Wen Jian Zhang 《Space(Science & Technology)》 EI 2024年第1期663-673,共11页
In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote ... In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite.However,the signals that generate RFI are diverse,and the factors that influence remote sensing observation data are complex.Existing algorithms often target specific hypothetical conditions,lacking general applicability,which frequently leads to an important gap between the nominal performance of the literature and practical applications,posing great challenges to data labeling work.To address this problem,this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling,algorithm modeling,and RFI energy modeling.Three typical RFI detection algorithms were selected,and the simulation scene was divided into 3 typical scenes:ocean,land,and sea–land scenes,and RFI was analyzed in terms of weak,moderate,strong,and extremely strong based on energy.Through simulation analysis and evaluation of RFI detection algorithms,lookup tables for algorithm selection,detection rate,and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario.These lookup tables have universal guiding significance and provide reliability assurance in complex situations. 展开更多
关键词 ocean salinity satellite remote sensingscholars radio frequency interference rfi satellite rfi detection remote sensing detection labeling algorithms remote sensing observation remote sensing data smos satellitehoweverthe
Combining TDLAS and multi-fusion algorithms for methane gas concentration detection 认领 引用
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作者 SHI Guojun SONG Xinmin DONG Taiji 《Optoelectronics Letters》 EI 2024年第6期353-359,共7页
High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration inf... High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively. 展开更多
关键词 Combining TDLAS and multi-fusion algorithms for methane gas concentration detection TDLAS
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Piecewise linear detection for direct superposition modulation 认领 引用 被引量：1
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作者 Martin Damrath Peter Adam Hoeher Gilbert J.M. Forkel 《Digital Communications and Networks》 SCIE 2018年第2期98-105,共8页
Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases expon... Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation （DSM）, is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection. 展开更多
关键词 Digital modulation Demodulation Detection algorithms Linear approximation
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Automatic Recognition Algorithm of Pavement Defects Based on S3M and SDI Modules Using UAV-Collected Road Images 认领 引用
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作者 Hongcheng Zhao Tong Yang +1 位作者 Yihui Hu Fengxiang Guo 《Structural Durability & Health Monitoring》 EI 2026年第1期121-137,共17页
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-... With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning. 展开更多
关键词 Pavement defects state space model UAV detection algorithm image processing
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Symbol detection based on Voronoi surfaces with emphasis on superposition modulation 认领 引用 被引量：1
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作者 Martin Damrath Peter Adam Hoeher Gilbert J.M. Forkel 《Digital Communications and Networks》 SCIE 2017年第3期141-149,共9页
A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially w... A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two- dimensional digital modulation schemes which cannot be split-up into real and imaginary parts （like phase shift keying and phase-shifted snperposition modulation （PSM）） is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection. 展开更多
关键词 Digital modulation Demodulation Detection algorithms Linear approximation
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Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine 认领 引用 被引量：5
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作者 Ding Meng Cao Yunfeng Wu Qingxian 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2013年第2期385-393,共9页
Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop cra... Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection. 展开更多
关键词 Crater candidate region Crater detection algorithm Kanade–Lucas–Tomasi detector Least squares support vector machine Matrixization
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Sequential Similarity Detection Algorithm Based on Image Edge Feature 认领 引用 被引量：5
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作者 马国红 王聪 +1 位作者 刘沛 朱书林 《Journal of Shanghai Jiaotong university(Science)》 EI 2014年第1期79-83,共5页
： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorith... ： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm. 展开更多
关键词 welding image feature matching sequential similarity detection algorithm(SSDA) self-adaption value
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A Detection Method of WLAN Security Mechanisms Based on MAC Frame Resolution 认领 引用 被引量：2
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作者 LI June YUAN Kai +5 位作者 ZHOU Liang HAN Lifang LI Ling WANG Zhihao LIU Yinbin HUANG Wenbin 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2017年第2期93-102,共10页
Security mechanism detection is not only an important content of vulnerabilities evaluation but also the foundation of key strength test for wireless local area network （WLAN）. This paper analyzes the specifications... Security mechanism detection is not only an important content of vulnerabilities evaluation but also the foundation of key strength test for wireless local area network （WLAN）. This paper analyzes the specifications of WLAN security mechanisms and points out the defects in design of security mechanisms detection algorithm based on the standards. By capturing and analyzing a large number of Beacon frames from different vendor＇s access points （APs）, we summarize the relevant fields and information elements in a Beacon frame, and present their values or status when an AP is set to every specific security mechanism. A detection algorithm of WLAN security mechanisms is proposed based on the experimental study result and the pseudo code of a reference implementation for the algorithm is designed. The validity of the algorithm is illustrated by examples, which shows it can detect every WLAN security mechanism accurately. 展开更多
关键词 IEEE 802.11 security mechanism detection algorithm MAC frame resolution vulnerabilities evaluation
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Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision 认领 引用 被引量：3
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作者 LI Chengzu WEI Kehan +4 位作者 ZHAO Yingbo TIAN Xuehui QIAN Yang ZHANG Lu WANG Rongwu 《Journal of Donghua University(English Edition)》 CAS 2024年第4期416-427,共12页
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki... Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production. 展开更多
关键词 defect detection nonwoven materials deep learning object detection algorithm transfer learning halfprecision quantization
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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net 认领 引用
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作者 Erssa Arif Syed Khuram Shahzad +3 位作者 Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi 《Computers, Materials & Continua》 SCIE EI 2022年第9期4615-4630,共16页
The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolv... The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms. 展开更多
关键词 Detection algorithms machine learning machine vision video surveillance
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An efficient approach for shadow detection based on Gaussian mixture model 认领 引用 被引量：4
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作者 韩延祥 张志胜 +1 位作者 陈芳 陈恺 《Journal of Central South University》 SCIE EI CAS 2014年第4期1385-1395,共11页
An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and fore... An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step. 展开更多
关键词 shadow detection Gaussian mixture model EM algorithm
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Density-based trajectory outlier detection algorithm 认领 引用 被引量：13
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作者 Zhipeng Liu Dechang Pi Jinfeng Jiang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第2期335-340,共6页
With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr... With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm. 展开更多
关键词 density-based algorithm trajectory outlier detection（TRAOD） partition-and-detect framework Hausdorff distance
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AIRS Cloud Detection Scheme Based on FOV 认领 引用
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作者 陈靖 李刚 王根 《Meteorological and Environmental Research》 2010年第2期88-90,共3页
6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view conta... 6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view contaminated by cloud,which lays a solid foundation for application of AIRS data in 3-dimensional variational assimilation system. 展开更多
关键词 AIRS Radiance Cloud contamination Mitch cloud detection algorithm China
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Monitoring Freeway Incident Detection Using a Hotelling T2 Control Chart 认领 引用
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作者 Joonse Lim Young Seon Jeong Youngsul Jeong 《Computer Technology and Application》 2012年第5期361-367,共7页
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec... In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections. 展开更多
关键词 Freeway incident incident detection algorithms Hotelling T2 control chart wavelet transforms feature selection.
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Comparative Study of Four Classification Techniques for the Detection of Threats in Baggage from X-Ray Images 认领 引用
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作者 Boka Trinité Konan Hyacinthe Kouassi Konan +1 位作者 Jules Allani Olivier Asseu 《Open Journal of Applied Sciences》 2024年第12期3490-3499,共10页
Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for deton... Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure. 展开更多
关键词 Deep Learning Baggage Control Convolutional Neural Networks Image Filtering Object Detection Algorithms X-Ray Images Autoencoder Random Forests
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A Practical Comparison between Signature Approach and Other Existing Approaches in Error Detection over TCP 认领 引用
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作者 Mohammed J. Bawaneh Mahmud S. Alkoffash +1 位作者 Shihadeh Alqrainy Hasan Muaidi 《Communications and Network》 2016年第1期31-38,共8页
Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. I... Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time. 展开更多
关键词 Error Detection Error Control Simulator Packet Size Detection Algorithms Signature Detection
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A fast detection algorithm for ceramic ball surface defects based on fringe reflection 认领 引用 被引量：4
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作者 SUN Ying FU Lu-hua WANG Zhong 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2020年第1期28-37,共10页
A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects bas... A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm. 展开更多
关键词 ceramic ball surface defect fringe reflection visual detection algorithm
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