Lightweight algorithm for strip steel surface defect detection based on feature enhancement
Authors: 
Chunmei Wang, Huan LiuAuthors Info & Claims
Chunmei Wang, Huan LiuAuthors Info & ClaimsPages 246 - 252
Abstract
Abstract: The small size of strip surface defects, dense defect distribution, and high background noise leads to the problems of poor detection accuracy and real-time detectability of general-purpose target detection algorithms in detecting strip surface defects. To solve the above problems, a lightweight algorithm based on feature enhancement for strip steel surface defect detection is proposed. The algorithm introduces the GhostConv module in the GhostNet network structure to replace the common convolution module in the feature extraction network and feature fusion network in the original YOLOv5s; meanwhile, the CBAM attention mechanism is introduced in the feature extraction network to enhance the feature extraction ability of the model and the expression ability of the defect information, to improve the detection accuracy of the model. The experimental results show that on the DEU-NET dataset, the improved YOLOv5s reduces the number of parameters and computation by 1.25M and 3GFLOPs, respectively, and improves the mAP from 78.7% to 80.3% by 1.6 percentage points. Experiments are conducted on the pavement defect dataset and VOC2012 dataset, and the results show that the improved algorithm has good robustness. It also has superiority in detection accuracy and detection speed compared with the current mainstream target detection algorithm for strip surface defect detection.
References
[1]
Zeng N, Wu P, Wang Z, A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-14. https://doi.org/10.1109/TIM.2022.3153997
[2]
Chen M, Yu L, Zhi C, Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization[J]. Computers in Industry, 2022, 134: 103551. https://doi.org/10.1016/j.compind.2021.103551
[3]
Pourkaramdel Z, Fekri-Ershad S, Nanni L. Fabric defect detection based on completed local quartet patterns and majority decision algorithm[J]. Expert Systems with Applications, 2022, 198: 116827. https://doi.org/10.1016/j.eswa.2022.116827
[4]
Liu Q, Wang C, Li Y, A fabric defect detection method based on deep learning[J]. IEEE Access, 2022, 10: 4284-4296. https://doi.org/10.1109/ACCESS.2021.3140118
[5]
Liang F, Zhou Y, Chen X, Review of target detection technology based on deep learning[C]//Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence. 2021: 132-135. https://doi.org/10.1145/3448218.3448234
[6]
Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.
[7]
Zhang K, Shen H. Solder joint defect detection in the connectors using improved faster-rcnn algorithm[J]. Applied Sciences, 2021, 11(2): 576. https://doi.org/10.3390/app11020576
[8]
Yang Aimin, Jiang Tianyyu, Han Yang, Research on application of on-line melting in-situ visual inspection of iron ore powder based on Faster R-CNN[J]. Alexandria Engineering Journal, 2022, 61(11): 8963-8971. https://doi.org/10.1016/j.aej.2022.02.034
[9]
Kumar A, Manikandan R. Brain Tumor Detection Using Deep Neural Network-Based Classifier[C]//International Conference on Innovative Computing and Communications. Springer, Singapore, 2022: 173-181. https://doi.org/10.1007/978-981-16-2594-7_14
[10]
Guo F, Qian Y, Rizos D, Automatic rail surface defects inspection based on Mask R-CNN[J]. Transportation research record, 2021, 2675(11): 655-668. https://doi.org/10.1177/03611981211019034
[11]
Jiang P, Ergu D, Liu F, A Review of Yolo algorithm developments[J]. Procedia Computer Science, 2022, 199: 1066-1073. https://doi.org/10.1016/j.procs.2022.01.135
[12]
Cheng R. A survey: Comparison between Convolutional Neural Network and YOLO in image identification[C]//Journal of Physics: Conference Series. IOP Publishing, 2020, 1453(1): 012139. https://doi.org/10.1088/1742-6596/1453/1/012139
[13]
Zhou F, He F, Gui C, SAR target detection based on improved SSD with saliency map and residual network[J]. Remote Sensing, 2022, 14(1): 180. https://doi.org/10.3390/rs14010180
[14]
Qian H, Wang H, Feng S, FESSD: SSD target detection based on feature fusion and feature enhancement[J]. Journal of Real-Time Image Processing, 2023, 20(1): 2. https://doi.org/10.1007/s11554-023-01258-y
[15]
LIU Q,LEI J S. Detection of steel surface defects based on improved deep network[J].Computer Engineering and Design,2022,43(09):2654-2661. https://doi.org/10.16208/j.issn1000-7024.2022.09.033
[16]
Cheng J Y, Duan X H, Zhu W. Research on Metal Surface Defect Detection by Improved YOLOv3 [J]. Computer Engineering and Application,2021,57(19):252-258. https://doi.org/10.3778/j.issn.1002-8331.2104-0324
[17]
Kou X, Liu S, Cheng K, Development of a YOLO-V3-based model for detecting defects on steel strip surface[J]. Measurement, 2021, 182: 109454. https://doi.org/10.1016/j.measurement.2021.109454
[18]
Guo S, Zhong P, Sun Y, Fast detection algorithm for surface defects of metal parts based on YOLOv4-mobilenet network[C]//International Conference on Intelligent Equipment and Special Robots (ICIESR 2021). SPIE, 2021, 12127: 528-534. https://doi.org/10.1117/12.2625428
[19]
Li M J, Wang H, Wan Z B. Surface defect detection of steel strips based on improved YOLOv4[J]. Computers and Electrical Engineering, 2022, 102: 108208. https://doi.org/10.1016/j.compeleceng.2022.108208
[20]
Han K, Wang Y, Tian Q, Ghostnet: More featuresfrom cheap operations[C]//Proceedings of the IEEE/CVFconference on computer vision and pattern recognition(CVPR). Seattle, WA, Jun 13-19, 2020. Piscataway: IEEE,2020: 1580-1589.
[21]
Woo S, Park J, Lee J Y, Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3-19. https://doi.org/10.1007/978-3-030-01234-2_1.
[22]
Li Xin,Wang Cheng,Li Bin,et al. Improved steel surface defect detection algorithm for YOLOv5[J].Journal of Air Force Engineering University(Natural Science Edition),2022,23(02):26-33.
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Published In
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
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Published: 14 June 2024
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AIPR 2023
AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 22 - 24, 2023
Xiamen, China
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