• Chinese Journal of Lasers
  • Vol. 49, Issue 21, 2104005 (2022)
Song Cheng1, Honggang Yang1, Xueqian Xu1, Min Li2, and Yunxia Chen1、*
Author Affiliations
  • 1School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China
  • 2Shanghai University of Electric Power, Shanghai 201306, China
  • show less
    DOI: 10.3788/CJL202249.2104005 Cite this Article Set citation alerts
    Song Cheng, Honggang Yang, Xueqian Xu, Min Li, Yunxia Chen. Improved Lightweight X-Ray Aluminum Alloy Weld Defects Detection Algorithm Based on YOLOv5[J]. Chinese Journal of Lasers, 2022, 49(21): 2104005 Copy Citation Text show less
    References

    [1] Wang J R, Wang G T, Yang B et al. Summary of research on application of deep learning in weld defect detection[J]. Mechanical & Electrical Engineering Technology, 50, 65-68(2021).

    [2] Liu Y, Lei Y B, Fan J L et al. Survey on image classification technology based on small sample learning[J]. Acta Automatica Sinica, 47, 297-315(2021).

    [3] Zhang H, Zhang Z Q, Chen Y R et al. Application progress and Prospect of nondestructive testing technology for defects of industrial castings[J]. Acta Automatica Sinica, 48, 1-22(2022).

    [4] Wang S Y, Gao W X, Zhang X S. Summary of defect detection algorithms for X-ray weld image[J]. Hot Working Technology, 49, 1-8(2020).

    [5] Zeng W, Wang H T, Tian G Y et al. Research on laser ultrasound imaging in detection of austenitic stainless steel weld[J]. Chinese Journal of Lasers, 41, 0703004(2014).

    [6] Han J J, Zhou J P, Xue R L et al. Surface morphology reconstruction and quality evaluation of pipeline weld based on line structured light[J]. Chinese Journal of Lasers, 48, 1402010(2021).

    [7] Ma Q, Li L T, Geng Z Q et al. Research on the welding flaw detection method based on enhanced YOLOv3 model[J]. Shanghai Auto, 56-62(2021).

    [8] Xing J J, Jia M P. A convolutional neural network-based method for workpiece surface defect detection[J]. Measurement, 176, 109185(2021).

    [9] Li Y D, Dong H, Li H G et al. Multi-block SSD based on small object detection for UAV railway scene surveillance[J]. Chinese Journal of Aeronautics, 33, 1747-1755(2020).

    [10] Huang Z, Yin Z Y, Ma Y et al. Mobile phone component object detection algorithm based on improved SSD[J]. Procedia Computer Science, 183, 107-114(2021).

    [11] Sun X H, Gu J N, Huang R. A modified SSD method for electronic components fast recognition[J]. Optik, 205, 163767(2020).

    [12] Sun X D, Wu P C, Hoi S C H. Face detection using deep learning: an improved faster RCNN approach[J]. Neurocomputing, 299, 42-50(2018).

    [13] Mansour R F, Escorcia-Gutierrez J, Gamarra M et al. Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model[J]. Image and Vision Computing, 112, 104229(2021).

    [14] Jiang D, Li G F, Tan C et al. Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model[J]. Future Generation Computer Systems, 123, 94-104(2021).

    [15] Liu W M, Zou X Y. Detection of bolts on apron boards of EMUs based on improved YOLOv2[J]. Railway Standard Design, 66, 161-166(2022).

    [16] Kavitha N, Chandrappa D N. Optimized YOLOv2 based vehicle classification and tracking for intelligent transportation system[J]. Results in Control and Optimization, 2, 100008(2021).

    [17] Shi Z H, Chen J. Research on the detection of workpiece surface defects based on YoloV3[J]. Machinery Design & Manufacture, 62-65, 69(2021).

    [18] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).

    [19] Ju M R, Luo H B, Wang Z B et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 39, 0715004(2019).

    [20] Li Y, Lu Y J, Chen J. A deep learning approach for real-time rebar counting on the construction site based on YOLOv3 detector[J]. Automation in Construction, 124, 103602(2021).

    [21] Lai W H, Zhou M G, Hu F et al. Coal gangue detection based on multi-spectral imaging and improved YOLO v4[J]. Acta Optica Sinica, 40, 2411001(2020).

    [22] Gao W, Zhou C, Guo M F. Insulator defect identification via improved YOLOv4 and SR-GAN algorithm[J]. Electric Machines and Control, 25, 93-104(2021).

    [23] Yu Z W, Shen Y G, Shen C K. A real-time detection approach for bridge cracks based on YOLOv4-FPM[J]. Automation in Construction, 122, 103514(2021).

    [24] Guo F, Qian Y, Shi Y F. Real-time railroad track components inspection based on the improved YOLOv4 framework[J]. Automation in Construction, 125, 103596(2021).

    [25] He D Q, Zou Z H, Chen Y J et al. Obstacle detection of rail transit based on deep learning[J]. Measurement, 176, 109241(2021).

    [26] Zhang Y S, Yang G W, Wang Q Q et al. Weld feature extraction based on fully convolutional networks[J]. Chinese Journal of Lasers, 46, 0302002(2019).

    [27] Sun M J, Lü C Z, Han Y H et al. Weakly supervised surface defect detection based on attention mechanism[J]. Journal of Computer-Aided Design & Computer Graphics, 33, 920-928(2021).

    [28] Wu Y J, Hua X, Wang L R et al. Method of substation equipment defect detection based on attention mechanism learning[J]. Computer and Modernization, 7-12, 17(2021).

    [29] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

    [30] Yang Y, Li L W, Gao S Y et al. Objects detection from high-resolution remote sensing imagery using training-optimized YOLOv3 network[J]. Laser & Optoelectronics Progress, 58, 1601002(2021).

    Song Cheng, Honggang Yang, Xueqian Xu, Min Li, Yunxia Chen. Improved Lightweight X-Ray Aluminum Alloy Weld Defects Detection Algorithm Based on YOLOv5[J]. Chinese Journal of Lasers, 2022, 49(21): 2104005
    Download Citation