1 |
陶显,侯伟,徐德.基于深度学习的表面缺陷检测方法综述[J].自动化学报,2021,47(5):1017-1034.
|
|
TAO X, HOU W, XU D. A survey of surface detect detection methods based on deep learning[J]. Acta Automatica Sinica, 2021, 47(5): 1017-1034.
|
2 |
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 and Machine Intelligence, 2017, 39(6):1137-1149. 10.1109/tpami.2016.2577031
|
3 |
REDMON J, FARHADI A. YOLOv3: An incremental improvement [EB/OL]. (2018-04-08) [2022-07-28]. . 10.1109/cvpr.2017.690
|
4 |
BOCHKOVSKIV A, WANG C-Y, LIAO H-Y M. YOLOv4: optimal speed and accuracy of object detection [EB/OL]. (2020-04-23) [2022-08-03]. .
|
5 |
GE Z, LIU S, WANG F,et al. YOLOX: exceeding YOLO series in 2021 [EB/OL]. (2021-08-06) [2022-08-17]. .
|
6 |
WANG C-Y, BOCHKOVSKIV A, LIAO H-Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [EB/OL]. (2022-07-06) [2022-09-18]. . 10.1109/uv56588.2022.10185474
|
7 |
王海云,王剑平,罗付华.融合多层次特征Faster R-CNN的金属板带材表面缺陷检测研究[J].机械科学与技术,2021,40(2):262-269.
|
|
WANG H Y, WANG J P, LUO F H. Study on surface defect detection of metal sheet and strip using Faster R-CNN with multilevel feature [J]. Mechanical Science and Technology for Aerospace Engineering, 2021,40(2):262-269.
|
8 |
程婧怡,段先华,朱伟.改进YOLOv3的金属表面缺陷检测研究[J].计算机工程与应用,2021,57(19):252-258. 10.3778/j.issn.1002-8331.2104-0324
|
|
CHENG J Y, DUAN X H, ZHU W. Research on metal surface defect detection by improved YOLOv3 [J]. Computer Engineering and Applications, 2021, 57(19): 252-258. 10.3778/j.issn.1002-8331.2104-0324
|
9 |
李彬,汪诚,吴静,等.改进YOLOv4算法的航空发动机部件表面缺陷检测[J].激光与光电子学进展,2021,58(14): 1415004. 10.3788/lop202158.1415004
|
|
LI B, WANG C, WU J, et al. Surface defect detection of aeroengine components based on improved YOLOv4 algorithm [J]. Laser & Optoelectronics Progress, 2021, 58(14): 1415004. 10.3788/lop202158.1415004
|
10 |
陈德富,闫坤,熊经先,等.基于改进YOLOv4的工件表面缺陷检测方法[J].计算机应用,2022,42(S1):94-99.
|
|
CHEN D F, YAN K, XIONG J X, et al. Detection method of workpiece surface defects based on improved YOLOv4 [J]. Journal of Computer Applications, 2022, 42(S1): 94-99.
|
11 |
TANG Y, HAN K, GUO J, et al. GhostNetV2: enhance cheap operation with long-range attention [EB/OL]. (2022-11-23) [2023-01-16]. .
|
12 |
WOO S, PARK J, LEE J-Y, et al. CBAM: convolutional block attention module [C]// Proceedings of the 2018 European Conference on Computer Vision. Cham: Springer, 2018:3-19. 10.1007/978-3-030-01234-2_1
|
13 |
TONG Z, CHEN Y, XU Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism [EB/OL]. (2023-01-24) [2023-02-06]. .
|
14 |
HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations [C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020:1577-1586. 10.1109/cvpr42600.2020.00165
|
15 |
HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks [EB/OL]. (2019-05-16) [2022-12-28]. . 10.1109/cvpr.2018.00745
|
16 |
WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks [EB/OL]. (2020-04-07) [2023-01-05]. . 10.1109/cvpr42600.2020.01155
|
17 |
李嘉新,侯进,盛博莹,等.基于改进YOLOv5的遥感小目标检测网络[J].计算机工程, 2023, 49(9): 256-264.
|
|
LI J X, HOU J, SHEN B Y, et al. Remote sensing small object detection network based on improved YOLOv5 [J]. Computer Engineering, 2023, 49(9): 256-264.
|
18 |
程松,杨洪刚,徐学谦,等.基于YOLOv5的改进轻量型X射线铝合金焊缝缺陷检测算法[J].中国激光,2022,49(21):2104005. 10.3788/CJL202249.2104005
|
|
CHENG S, YANG H G, XU X Q, et al. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5 [J]. Chinese Journal of Lasers, 2022,49(21):2104005. 10.3788/CJL202249.2104005
|