[1] Chen H B, Xiao R Q, Chen S B. Progress and trends towards visual perception technologies in intelligent robotic welding[J]. Transactions of the China Welding Institution, 45, 1-9(2024).

[2] Zhang Z F, Chen S B, Zhang Y M et al. Research progress and prospect of welding intelligent monitoring technology[J]. Transactions of the China Welding Institution, 45, 10-20, 70(2024).

[3] Zhang D H, Hao X Y, Wang D C et al. An efficient lightweight convolutional neural network for industrial surface defect detection[J]. Artificial Intelligence Review, 56, 10651-10677(2023).

[4] Sassi P, Tripicchio P, Avizzano C A. A smart monitoring system for automatic welding defect detection[J]. IEEE Transactions on Industrial Electronics, 66, 9641-9650(2019).

[5] 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, 39, 1137-1149(2017).

[6] Chen Y B, Wang J R, Wang G T. Intelligent welding defect detection model on improved R-CNN[J]. IETE Journal of Research, 69, 9235-9244(2023).

[7] Wang X P, Zhang B X, Yu X H. Zoom in on the target network for the prediction of defective images and welding defects’ location[J]. NDT & E International, 143, 103059(2024).

[8] Dai Z, Liu X J, Pan Q. Defect identification algorithm for weld X-ray images based on the CCBFE-RCNN model[J]. Transactions of the China Welding Institution, 46, 24-33(2025).

[9] Wang X P, Wang X, Zhang B X et al. Binary classification of welding defect based on deep learning[J]. Science and Technology of Welding and Joining, 27, 407-417(2022).

[10] Farhadi A, Redmon J. YOLOv3: an incremental improvement[EB/OL]. https:∥arxiv.org/abs/1804.02767

[11] Zhang H Z, Yang R, Deng X et al. An infrared defect target detection algorithm based on improved SSD[J]. Laser & Infrared, 54, 1885-1893(2024).

[12] Wu L, Chu Y K, Yang H G et al. Sim-YOLOv8 object detection model for DR image defects in aluminum alloy welds[J]. Chinese Journal of Lasers, 51, 1602103(2024).

[13] Kwon J E, Park J H, Kim J H et al. Context and scale-aware YOLO for welding defect detection[J]. NDT & E International, 139, 102919(2023).

[14] Liu M Y, Chen Y P, Xie J M et al. LF-YOLO: a lighter and faster YOLO for weld defect detection of X-ray image[J]. IEEE Sensors Journal, 23, 7430-7439(2023).

[15] Pan K L, Hu H Y, Gu P. WD-YOLO: a more accurate YOLO for defect detection in weld X-ray images[J]. Sensors, 23, 8677(2023).

[16] Wang H J, Lin N, Lin Z C et al. X-ray pipe weld detection algorithm of improved YOLO[J]. Journal of Huaqiao University (Natural Sciences), 45, 766-775(2024).

[17] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https:?∥arxiv.org/abs/1706.03762

[18] Dai J F, Qi H Z, Xiong Y W et al. Deformable convolutional networks[C], 764-773(2017).

[19] Zhang X, Liu C, Yang D G et al. RFAConv: innovating spatial attention and standard convolutional operation[EB/OL]. https:?∥arxiv.org/abs/2304.03198v6

[20] Liu Y, Yuan K, Li T et al. NDT method for weld defects based on FMPVit transformer model[J]. IEEE Access, 11, 61390-61400(2023).

[21] Wang C Y, Yeh I H, Mark Liao H Y. YOLOv9: learning what you want to learn using programmable gradient information[EB/OL]. https:∥arxiv.org/abs/2402.13616

[22] Tan M X, Pang R M, Le Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(2020).

[23] Liu Y, Xia G, Wang Q et al. Infrared monitoring and identification of gas turbine oil pipe based on Faster RCNN[J]. Laser & Infrared, 53, 544-550(2023).

[24] Zhang J, Zhu B, Ji T. Infrared face detection algorithm based on MobileNet-SSD[J]. Laser & Infrared, 51, 107-113(2021).

[25] Jocher G, Chaurasia A, Stoken A et al. v6.2-YOLOv5 classification models, apple M[EB/OL]. https:?∥gitlab.com/ultralytics/yolov5/-/releases/v6.2