计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240400146-10.doi: 10.11896/jsjkx.240400146

• 图像处理&多媒体技术 • 上一篇    下一篇

结合特征复用和廉价操作的高精度光伏玻璃边部缺陷实时检测算法

丁绪星, 周学顶, 钱强, 任悦悦, 冯友宏   

  1. 安徽师范大学物理与电子信息学院 安徽 芜湖 241002
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 丁绪星(dxx200@ahnu.edu.cn)
  • 基金资助:
    国家自然科学基金(62071005)

High-precision and Real-time Detection Algorithm for Photovoltaic Glass Edge Defects Based onFeature Reuse and Cheap Operation

DING Xuxing, ZHOU Xueding, QIAN Qiang, REN Yueyue, FENG Youhong   

  1. School of Physics and Electronic Information,Anhui Normal University,Wuhu,Anhui 241002,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:DING Xuxing,born in 1971,professor,master’s supervisor.His main research interests include digital image proces-sing,computer vision,and object detection.
  • Supported by:
    National Natural Science Foundation of China(62071005).

PDF (PC)

39

摘要： 针对现有的缺陷检测算法计算量大、参数量多、检测速度慢、检测精度低等问题,文中提出了一种基于YOLOv5的高精度光伏玻璃边部缺陷实时检测算法。首先使用新设计的一种基于廉价操作和特征复用的稠密连接模块(New_DBlock(C))替代YOLOv5特征提取网络的C3模块,减少整个算法的计算量和参数量;其次使用融合了通道注意力机制SE(Squeeze-and-Excitation)的C2f_SE模块替换YOLOv5特征融合网络的C3模块,实现检测速度以及检测精度的提升;最后使用改进的YOLOv8解耦检测头取代YOLOv5的耦合检测头,提升算法的定位精度和分类精度。实验结果表明,改进后的算法mAP@0.5提升了1.0%,mAP@0.5:0.95提升了3.1%,计算量下降了48.1%,参数量下降了56.7%,检测速度提升了18.5%;与其他主流的YOLO和R-CNN系列算法相比,改进后的算法同样具有较高的检测精度、检测速度以及较低的计算量和参数量,适合光伏玻璃边部缺陷的实时检测。

关键词: 光伏玻璃, 缺陷检测, YOLOv5s, 目标检测, 注意力机制

Abstract: Aiming at the problems of large amount of calculation,large amount of parameters,slow detection speed and low detection accuracy of existing defect detection algorithms,this paper proposes a high-precision and real-time detection algorithm for photovoltaic glass edge defects based on YOLOv5.Firstly,a newly designed dense connection block(New_DBlock(C)) based on cheap operation and feature reuse is used to replace the C3 block of YOLOv5’s feature extraction network,which reduced the calculation amount and parameter amount of the whole algorithm.Secondly,the C2f_SE block fused with the channel attention mechanism SE(Squeeze-and-Excitation) is used to replace the C3 block of the YOLOv5’s feature fusion network to improve the detection speed and detection accuracy.Finally,the improved YOLOv8’s decoupling detection head is used to replace the coupling detection head of YOLOv5 to improve the positioning accuracy and classification accuracy of the algorithm.The experimental results show that the improved algorithm mAP@0.5 is increased by 1.0%,mAP@0.5:0.95 is increased by 3.1%,the amount of calculation is decreased by 48.1%,the amount of parameters is decreased by 56.7%,and the detection speed is increased by 18.5%.Compared with other mainstream YOLO and R-CNN series algorithms,the improved algorithm also has higher detection accuracy,detection speed,and lower amount of calculation and parameters,which is suitable for the real-time detection of photovoltaic glass edge defects.

Key words: Photovoltaic glass, Defect detection, YOLOv5s, Object detection, Attention mechanism

中图分类号: 

  • TP391
[1]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[2]HE K,GKIOXARI G,PIOTR D,et al.Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision.Venice,Italy:IEEE,2017:2961-2969.
[3]CAI Z W,VASCONCELOS N.Cascade R-CNN:delving intohigh quality object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA:IEEE,2018:6154-6162.
[4]LV Y F L,MA L,JIANG H Q.A Mobile Phone Screen Cover Glass Defect Detection MODEL Based on Small Samples Learning[C]//2019 IEEE 4th International Conference on Signal and Image Processing.Wuxi,China:IEEE,2019:1055-1059.
[5]CAO Y J,JIA L L,CHEN Y X,et al.Recent advances of generative adversarial networks in computer vision[J].IEEE Access,2018,7:14985-15006.
[6]LI L,Z.JIANG Z J,LI Y N.Surface Defect Detection Algorithm of Aluminum Based on Improved Faster RCNN[C]//2021 IEEE 9th International Conference on Information,Communication and Networks.Xi’an,China IEEE Press,2021:527-531.
[7]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA:IEEE Press,2016:770-778.
[8]LIU G,WANG F,HUI H.Application of Mask R-CNN in defect detection of industrial filter cloth[C]//2022 Global Conference on Robotics,Artificial Intelligence and Information Technology.Chicago,IL,USA:IEEE,2022:794-798.
[9]BAI M L,WANG M W.Fabric defect detection algorithm based on improved Cascade R-CNN[J].Computer Science,2023,50(6A):220300224-6.
[10]REZATOFIGHI H,TSOI N,GWAK J A,et al.Generalized intersection over union:a metric and a loss for bounding box regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach,CA,USA:IEEE,2019:658-666.
[11]JIANG P,ERGU D,LIU F,et al.A review of yolo algorithm developments[J].Procedia Computer Science,2022,199:1066-1073.
[12]TERVEN J,CORDOVA-ESPARZA D M,ROMERO-GONZALEZ J A.A comprehensive review of yolo architectures in computer vision:from yolov1 to yolov8 and yolo-nas[J].Machine Learning and Knowledge Extraction,2023,5(4):1680-1716.
[13]KANG C H,KIM S Y.Real-time object detection and segmentation technology:an analysis of the YOLO algorithm[J].JMST Advances,2023,5(2):69-76.
[14]HASAN R H,HASSOO R M,ABOUD I S.Yolo versions architecture:review[J].International Journal of Advances in Scientific Research and Engineering,2023,9(11):73-92.
[15]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotMultiBox detector[C]//European Conference on Computer Vision.Cham:Springer International Publishing,2016:21-37.
[16]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(2):318-327.
[17]HU H T,DU H C,WANG S Q,et al.Improved YOLOX method for detecting surface defects of drug blister aluminum foil[J].Journal of Graphics,2022,43(5):803-814.
[18]GE Z,LIU S,WANG F,et al.Yolox:exceeding yolo series in 2021[J].arXiv:2107.08430,2021.
[19]HAN D,PAN X,HAN Y,et al.Flatten transformer:visiontransformer using focused linear attention[C]//2023 IEEE/CVF International Conference on Computer Vision.Paris,France:IEEE,2023:5961-5971.
[20]LAN H,ZHU Z H,LUO R,et al.PCB defect detection algorithm of improved YOLOv8[C]//2023 8th International Conference on Image,Vision and Computing.Dalian,China:IEEE,2023:178-183.
[21]SOHAN M,SAI-RAM T,REDDY R,et al.A review onYOLOv8 and its advancements[C]//International Conference on Data Intelligence and Cognitive Informatics.Springer,Singapore:Springer,2024:529-545.
[22]LIU Z,ZHOU B.Research and application on the improved SSD chip defect inspection algorithm[C]//2021 IEEE International Conference on Consumer Electronics and Computer Engineering.Guangzhou,China:IEEE,2021:551-555.
[23]ZHOU B,JIANGP F,DUAN C,et al.Study on single background object detection oriented Improved-RetinaNet model and its application [J].Computer Science,2023,50(7):137-142.
[24]WANG X,GAO J S,HOU B J,et al.A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection[J].IEEE Sensors Journal,2022,22(21):20910-20920.
[25]HOU Q,ZHOU D,FENG J.Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashville,TN,USA:IEEE,2021:13713-13722.
[26]OLORUNSHOLA O E,IRHEBHUDE M E,EVWIEKPAEFE A E.A comparative study of YOLOv5 and YOLOv7 object detection algorithms[J].Journal of Computing and Social Informatics,2023,2(1):1-12.
[27]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City,USA:IEEE,2018:7132-7141.
[28]WU Y,CHEN Y,YUAN L,et al.Rethinking classification and localization for object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle,WA,USA:IEEE,2020:10186-10195.
[29]DAI J,LI Y,HE K,et al.R-FCN:object detection via region-based fully convolutional networks[C]//30th Conference on Neural Information Processing Systems.Barcelona,Spain:Curran Associates,Inc,2016,29:379-387.
[30]WANG C Y,BOCHKOVSKIY A,LIAO H Y M.YOLOv7:Trainablebag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Vancouver.Canada:IEEE,2023:7464-7475.
[31]ZENG Q,WEI D,ZHANG X,et al.MFAM-Net:a surface defect detection network for strip steel via multiscale feature fusion and attention mechanism[C]//2023 International Conference on New Trends in Computational Intelligence(NTCI).Qingdao,China:IEEE,2023:117-121.
[32]ZHANG Z D,ZHANG B,LAN Z C,et al.FINet:an insulator dataset and detection benchmark based on synthetic fog and improved YOLOv5[J].IEEE Transactions on Instrumentation and Measurement.2022,71:1-8.
[33]LI F,XI Q G.DefectNet:toward fast and effective defect detection[J]IEEE Transactions on Instrumentation and Measurement.2021,70:1-9.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发