Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 638-645.DOI: 10.11772/j.issn.1001-9081.2021020227
• Frontier and comprehensive applications • Previous Articles Next Articles
Pinxue WANG1,2, Shaobing ZHANG1,2,3(), Miao CHENG1,2,3, Lian HE1,3, Xiaoshan QIN1,2
Received:
2021-02-05
Revised:
2021-04-02
Accepted:
2021-04-12
Online:
2022-02-11
Published:
2022-02-10
Contact:
Shaobing ZHANG
About author:
WANG Pinxue, born in 1993, M. S. candidate. His research interests include few-shot learning, surface defect detection.
王品学1,2, 张绍兵1,2,3(), 成苗1,2,3, 何莲1,3, 秦小山1,2
通讯作者:
张绍兵
作者简介:
王品学(1993—),男,四川成都人,硕士研究生,CCF会员,主要研究方向:小样本学习、表面缺陷检测;CLC Number:
Pinxue WANG, Shaobing ZHANG, Miao CHENG, Lian HE, Xiaoshan QIN. Coin surface defect detection algorithm based on deformable convolution and adaptive spatial feature fusion[J]. Journal of Computer Applications, 2022, 42(2): 638-645.
王品学, 张绍兵, 成苗, 何莲, 秦小山. 基于可变形卷积和自适应空间特征融合的硬币表面缺陷检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 638-645.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021020227
模型 | 骨干网络 | mAP/% | F1/% | 帧率/(frame |
---|---|---|---|---|
Faster-RCNN | Resnet50 | 92.9 | 92.5 | 19 |
YOLOv3 | Darket53 | 89.5 | 89.2 | 34 |
YOLOv3-PAN | Darknet53 | 90.4 | 90.4 | 33 |
YOLOv3-tiny | Darknet19-Impoved | 75.4 | 73.9 | 217 |
DenseNet-tiny | DenseNet18 | 85.3 | 83.1 | 46 |
YOLOv3-ASFF | Darknet53 | 91.1 | 91.0 | 34 |
DCA-YOLO | Darknet53 | 92.8 | 92.4 | 32 |
Tab. 1 Comparison of different model detection results
模型 | 骨干网络 | mAP/% | F1/% | 帧率/(frame |
---|---|---|---|---|
Faster-RCNN | Resnet50 | 92.9 | 92.5 | 19 |
YOLOv3 | Darket53 | 89.5 | 89.2 | 34 |
YOLOv3-PAN | Darknet53 | 90.4 | 90.4 | 33 |
YOLOv3-tiny | Darknet19-Impoved | 75.4 | 73.9 | 217 |
DenseNet-tiny | DenseNet18 | 85.3 | 83.1 | 46 |
YOLOv3-ASFF | Darknet53 | 91.1 | 91.0 | 34 |
DCA-YOLO | Darknet53 | 92.8 | 92.4 | 32 |
模型分类 | 插入位置 | P/% | R/% | mAP/% | F1/% | 帧率/(frams |
---|---|---|---|---|---|---|
1 | D5输出后替换SPP中的3×3卷积 | 92.1 | 92.0 | 92.2 | 92.0 | 32 |
2.1 | 上采样前添加 | 92.3 | 92.5 | 92.8 | 92.4 | 32 |
2.2 | 在上采样前添加并在D3、D4后加Conv | 92.2 | 92.6 | 92.9 | 92.4 | 27 |
3 | D3、D4输出后 | 91.6 | 91.9 | 91.9 | 91.7 | 28 |
Tab. 2 Comparison of detection results of adding deformable convolution in different positions
模型分类 | 插入位置 | P/% | R/% | mAP/% | F1/% | 帧率/(frams |
---|---|---|---|---|---|---|
1 | D5输出后替换SPP中的3×3卷积 | 92.1 | 92.0 | 92.2 | 92.0 | 32 |
2.1 | 上采样前添加 | 92.3 | 92.5 | 92.8 | 92.4 | 32 |
2.2 | 在上采样前添加并在D3、D4后加Conv | 92.2 | 92.6 | 92.9 | 92.4 | 27 |
3 | D3、D4输出后 | 91.6 | 91.9 | 91.9 | 91.7 | 28 |
序号 | Mosaic数据增强 | Mish激活函数 | 动态类别权重 | 拉伸先验锚框 | mAP/% | F1/% |
---|---|---|---|---|---|---|
1 | 92.2 | 91.9 | ||||
2 | 91.1 | 91.2 | ||||
3 | 92.3 | 92.0 | ||||
4 | 91.6 | 91.7 | ||||
5 | 92.3 | 92.1 | ||||
6 | 92.5 | 92.1 | ||||
7 | 92.8 | 92.4 |
Tab. 3 Influence of Mosaic data augmentation, activation function, dynamic category weight and stretching priori anchor box on detection results
序号 | Mosaic数据增强 | Mish激活函数 | 动态类别权重 | 拉伸先验锚框 | mAP/% | F1/% |
---|---|---|---|---|---|---|
1 | 92.2 | 91.9 | ||||
2 | 91.1 | 91.2 | ||||
3 | 92.3 | 92.0 | ||||
4 | 91.6 | 91.7 | ||||
5 | 92.3 | 92.1 | ||||
6 | 92.5 | 92.1 | ||||
7 | 92.8 | 92.4 |
1 | 工业互联网产业联盟.工业智能白皮书2020[EB/OL]. (2020-04-26)[2020-07-01]. . |
2 | 张建伟,雷霖,余化鹏,等.基于形态学的硬币镜面区域缺陷检测算法研究[J].成都大学学报(自然科学版), 2016, 35(3): 245-247, 259. |
ZHANG J W, LEI L, YU H P, et al. Research on defect detection algorithm based on coin mirror surface[J]. Journal of Chengdu University (Natural Science), 2016, 35(3): 245-247, 259. | |
3 | KIM J M, RYOO H J. Inspection of coin surface defects using multiple eigen spaces[J]. The Journal of the Korea Contents Association, 2011, 11(3): 18-25. 10.5392/jkca.2011.11.3.018 |
4 | GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 580-587. 10.1109/cvpr.2014.81 |
5 | GIRSHICK R. Fast R-CNN [C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 1440-1448. 10.1109/iccv.2015.169 |
6 | REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2015: 91-99. |
7 | HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988. 10.1109/iccv.2017.322 |
8 | LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multiBox detector [C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS9905. Cham: Springer, 2016: 21-37. |
9 | REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 779-788. 10.1109/cvpr.2016.91 |
10 | 金侠挺,王耀南,张辉,等.基于贝叶斯CNN和注意力网络的钢轨表面缺陷检测系统[J].自动化学报, 2019, 45(12): 2312-2327. 10.16383/j.aas.c190143 |
JIN X T, WANG Y N, ZHANG H, et al. DeepRail: automatic visual detection system for railway surface defect using Bayesian CNN and attention network[J]. Acta Automatica Sinica, 2019, 45(12): 2312-2327. 10.16383/j.aas.c190143 | |
11 | 徐镪,朱洪锦,范洪辉,等.改进的YOLOv3网络在钢板表面缺陷检测研究[J].计算机工程与应用, 2020, 56(16): 265-272. 10.3778/j.issn.1002-8331.2003-0232 |
XU Q, ZHU H J, FAN H H, et al. Study on detection of steel plate surface defects by improved YOLOv3 network[J]. Computer Engineering and Applications, 2020, 56(16): 265-272. 10.3778/j.issn.1002-8331.2003-0232 | |
12 | REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2021-01-23]. . 10.1109/cvpr.2018.00430 |
13 | LIU S T, HUANG D, WANG Y H. Learning spatial fusion for single-shot object detection[EB/OL]. (2019-11-25) [2021-01-23]. . 10.3390/rs11212484 |
14 | 陈静,毛莺池,陈豪,等.基于改进单点多盒检测器的大坝缺陷目标检测方法[J].计算机应用, 2021, 41(8): 2366-2372. |
CHEN J, MAO Y C, CHEN H, et al. Dam defect object detection method based on improved single shot multibox detector[J]. Journal of Computer Applications, 2021, 41(8): 2366-2372. | |
15 | DAI J F, QI H Z, XIONG Y W, et al. Deformable convolutional networks [C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 764-773. 10.1109/iccv.2017.89 |
16 | LONG X, DENG K P, WANG G Z, et al. PP-YOLO: an effective and efficient implementation of object detector[EB/OL]. (2020-08-03) [2021-01-23]. . |
17 | ZHU X Z, HU H, LIN S, et al. Deformable ConvNets v2: more deformable, better results [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9300-9308. 10.1109/cvpr.2019.00953 |
18 | LEI T, WANG R S, ZHANG Y X, et al. DefED-Net: deformable encoder-decoder network for liver and liver tumor segmentation[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021(Early Access): 1-1. 10.1109/TRPMS.2021.3059780 |
19 | LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 936-944. 10.1109/cvpr.2017.106 |
20 | LIN T Y, MAIRE M, BELONGIE S J. et al. Microsoft COCO: common objects in context[C]// Proceedings of the 2014 European Conference on Computer Vision.Piscataway: IEEE, 2014: 740-755. 10.1007/978-3-319-10602-1_48 |
21 | 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. 10.1109/tpami.2018.2858826 |
22 | LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 8759-8768. 10.1109/cvpr.2018.00913 |
[1] | Zhangjian JI, Ming ZHANG, Zilong WANG. High-precision object detection algorithm based on improved VarifocalNet [J]. Journal of Computer Applications, 2023, 43(7): 2147-2154. |
[2] | Yongxiang GU, Xin LAN, Boyi FU, Xiaolin QIN. Object detection algorithm for remote sensing images based on geometric adaptation and global perception [J]. Journal of Computer Applications, 2023, 43(3): 916-922. |
[3] | Guohuan HE, Jiangping ZHU. WT-U-Net++: surface defect detection network based on wavelet transform [J]. Journal of Computer Applications, 2023, 43(10): 3260-3266. |
[4] | SUN Zeqiang, CHEN Bingcai, CUI Xiaobo, WANG Lei, LU Yanuo. Strip steel surface defect detection by YOLOv5 algorithm fusing frequency domain attention mechanism and decoupled head [J]. Journal of Computer Applications, 2023, 43(1): 242-249. |
[5] | GUO Zihao, DONG Lele, QU Zhijian. Arthropod object detection method based on improved Faster RCNN [J]. Journal of Computer Applications, 2023, 43(1): 88-97. |
[6] | Hanqing LIU, Xiaodong KANG, Fuqing ZHANG, Xiuyuan ZHAO, Jingyi YANG, Xiaotian WANG, Mengfan LI. Image detection algorithm of cerebral arterial stenosis by improved Libra region-convolutional neural network [J]. Journal of Computer Applications, 2022, 42(9): 2909-2916. |
[7] | Yajiao LIU, Haitao YU, Jiang WANG, Lifeng YU, Chunhui ZHANG. Surface detection algorithm of multi-shape small defects for section steel based on deep learning [J]. Journal of Computer Applications, 2022, 42(8): 2601-2608. |
[8] | Yi ZHUANG, Haitao ZHAO. Proposal-based aggregation network for single object tracking in 3D point cloud [J]. Journal of Computer Applications, 2022, 42(5): 1407-1416. |
[9] | CHEN Jing, MAO Yingchi, CHEN Hao, WANG Longbao, WANG Zicheng. Dam defect object detection method based on improved single shot multibox detector [J]. Journal of Computer Applications, 2021, 41(8): 2366-2372. |
[10] | SHI Yangxiao, ZHANG Jun, CHEN Peng, WANG Bing. Classification of steel surface defects based on lightweight network [J]. Journal of Computer Applications, 2021, 41(6): 1836-1841. |
[11] | ZHANG Jing'ai, WANG Jiangtao. Magnetic tile surface quality recognition based on multi-scale convolution neural network and within-class mixup operation [J]. Journal of Computer Applications, 2021, 41(1): 275-279. |
[12] | HUANG Dongyan, LI Lang. Optimal bitcoin transaction fee payment strategy based on queuing game [J]. Journal of Computer Applications, 2020, 40(9): 2646-2649. |
[13] | BIAN Xiaoyong, JIANG Peiling, ZHAO Min, DING Sheng, ZHANG Xiaolong. Multi-branch neural network model based weakly supervised fine-grained image classification method [J]. Journal of Computer Applications, 2020, 40(5): 1295-1300. |
[14] | ZHAO Yulong, NIU Baoning, LI Peng, FAN Xing. Blockchain enhanced lightweight node model [J]. Journal of Computer Applications, 2020, 40(4): 942-946. |
[15] | FAN Li, ZHENG Hong, HUANG Jianhua, LI Zhongcheng, JIANG Yahui. Cooperative evolution method for blockchain mining pool based on adaptive zero-determinant strategy [J]. Journal of Computer Applications, 2019, 39(3): 918-923. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||