[1] [1] WEI R, BI Y. Research on recognition technology of aluminum profile surface defects based on deep learning［J］. Materials, 2019, 12(10):1681.

[4] [4] LIU Z H, SHI H B, ZHOU X F. Aluminum profile type recognition based on texture features［J］. Applied Mechanics and Materials, 2014, 556:2846-2851.

[5] [5] CHONDRONASIOS A, POPOV I, JORDANOV I. Feature selection for surface defect classification of extruded aluminum profiles［J］. The International Journal of Advanced Manufacturing Technology, 2016, 83:33-41.

[6] [6] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.

[7] [7] REDMON J, FARHADI A. Yolov3: An incremental improvement［EB/OL］. (2018-04-08) ［2023-06-08］. https://arxiv.org/abs/1804.02767.

[8] [8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection［EB/OL］. (2020-04-23) ［2023-06-08］. https://arxiv.org/abs/2004.10934.

[10] [10] GE Z, LIU S, WANG F, et al. YOLOX: Exceeding YOLO series in 2021［EB/OL］. (2021-07-18) ［2023-06-08］. https://arxiv.org/abs/2107.08430.

[12] [12] LIU X, HE W, ZHANG Y, et al. Effect of dual-convolutional neural network model fusion for Aluminum profile surface defects classification and recognition［J］. Mathematical Biosciences and Engineering, 2022, 19(1):997-1025.

[13] [13] NEUHAUSER F M, BACHMANN G, HORA P. Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks［J］. International Journal of Material Forming, 2020, 13:591-603.

[14] [14] WU D, SHEN X, CHEN L. Detection of defects on aluminum profile surface based on improved YOLO［C］//2022 Prognostics and Health Management Conference, May 27-29, 2022, London, United Kingdom. New York: IEEE, 2022:468-472.

[16] [16] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design ［C］ //IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021:13708-13717.

[18] [18] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation［C］//IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018:8759-8768.

[19] [19] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition［C］//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016:770-778.

[20] [20] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression［J］. Neurocomputing, 2022, 506:146-157.