Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

NAVIGATE

Table of Contents

STATISTICS

Viewed1764

Downloads1106

Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

[HTML] PDF下载 (1106)

[1]JIA Yunfei,ZHENG Hongmu,LIU Shanliang.Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):31-38.[doi:10.13705/j.issn.1671-6833.2022.05.001]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 05 Page number: 31-38 Column: Public date: 2022-08-22

Title:: Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

Author(s):: JIA Yunfei; ZHENG Hongmu; LIU Shanliang; School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Keywords:: surface defect; object detection; light weight; YOLOv5s; tensorRT

CLC:: TP391. 7

DOI:: 10.13705/j.issn.1671-6833.2022.05.001

Abstract:: In order to reduce the intelligent cost in the enterprise, the hardware equipment with low cost and low computing power was used to detect the defects of products through the object detection algorithm model in deep learning. Based on the YOLOv5s network in target detection, this study adopts the idea of structure cutting, sparsely training the network based on the BN layer, and cuting the sparsely trained model corresponding to the layer with small weight value, so as to reduce the number of calculation parameters and the size of model file and to achieve the effect of lightweight. Finally, the trained pruning model was hierarchically fused using NVIDIA′s accelerated framework TensorRT to realize the reasoning acceleration effect. The experimental results showed that the weight file size of this model was reduced by about 70% compared with the original YOLOv5s model, and the detection accuracy on the public dataset NEU-DET reached 74.2%. In the high-performance experimental platform built in this study, the single graph inference speed was improved by 11.3% compared with the original model, and the network had no accuracy loss. In the low-performance experimental platform compared with the original network model, the inference speed of this model increased by 165%, which was more significantly improved than the results in the high-performance experimental platform, indicating that this model perform well in low computing power hardware devices. Then the model was tested by using the open top view data set of submersible pump impeller. At last, the inference acceleration framework TensorRT is used to accelerate the model in this study, and the inference time of single figure 5.8 ms can be achieved on the high-performance experimental platform. The experimental results showed that the inference speed of this model could be greatly improved on low computing power hardware equipment, which could help enterprises reduce their budget.

References:: [1] HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[ J] . IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9) :1904-1916.
[2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [ C] ∥2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: ACM, 2014: 580-587.
[3] REN S Q, HE K M, GIRSHICK R, et al. Faster RCNN: towards real-time object detection with regio proposal networks[ J] . IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6) : 1137 -1149.
[4] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB / OL] . ( 2018 - 04 - 08) [ 2021 - 09- 01] . https: / / arxiv. org / abs/ 1804. 02767.
[5] ZHOU P, NI B B, GENG C, et al. Scale-transferrable object detection[ C] / / 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 528-537.
[6] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[ C] / / European Conference on Computer Vision. Cham: Springer, 2016:21-37.
[7] 程旭, 宋晨, 史金钢, 等. 基于深度学习的通用目标检测研究综述 [ J] . 电子学报, 2021, 49 ( 7) : 1428-1438.
CHENG X, SONG C, SHI J G, et al. A survey of generic object detection methods based on deep learning [J]. Acta electronica sinica,2021,49(7): 1428-1438.
[8] 郝用兴, 李泽坤, 张太萍, 等. 改进 Faster R-CNN 对铝型材表面瑕疵的检测 [ J] . 工具技术, 2021, 55(3) : 76-80.
HAO Y X, LI Z K, ZHANG T P, et al. Detection of surface defect of aluminum profile by improved faster R-CNN [ J ] . Tool engineering, 2021, 55 ( 3 ) : 76-80.
[9] 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.
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] LI Y, XU J B. Electronic product surface defect detection based on a MSSD network[ C] / / 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference. Piscataway: IEEE, 2020: 773-777.
[11] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C] / / 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 779-788.
[12] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C] / / 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6517-6525.
[13] 陈友升, 周介祺, 梁敏健, 等. 基于 YOLOv5 视觉感知的实时叉车驾驶操作行为识别方法[ J] . 自动化与信息工程, 2021, 42(3) : 21-26.
CHEN Y S, ZHOU J Q, LIANG M J, et al. Real time forklift driving operation behavior recognition method based on YOLOv5 visual perception [ J] . Automation & information engineering, 2021, 42(3) : 21-26.
[14] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB / OL] . ( 2015 - 02 - 11) [ 2021 - 09 - 16] . https: / / arxiv. org / abs/ 1502. 03167.
[15] SHI X H, HU J, LEI X Y, et al. Detection of flying birds in airport monitoring based on improved YOLOv5 [C] / / 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). Piscataway: IEEE, 2021: 1446-1451.
[16] DETTMERS T, ZETTLEMOYER L. Sparse networks from scratch: faster training without losing performance [EB / OL] . (2019-06-13) [ 2021- 09- 14] . https: / / arxiv. org / abs/ 1907. 04840.
[17] 周立君, 刘宇, 白璐, 等. 使用 TensorRT 进行深度学习推理[ J] . 应用光学, 2020, 41(2) : 337-341. ZHOU L J, LIU Y, BAI L, et al. Using TensorRT for deep learning and inference applications [ J] . Journal of applied optics, 2020, 41(2) : 337-341.
[18] 张震, 李浩方, 李孟洲, 等. 改进 YOLOv3 算法与人体信息数据融合的视频监控检测方法[ J] . 郑州大学学报(工学版) , 2021, 42(1) : 28-34.
ZHANG Z, LI H F, LI M Z, et al. Video surveillance detection method based on improved YOLOv3 algorithm and human body information data fusion[J]. Journal of Zhengzhou university ( engineering science), 2021, 42 (1): 28-34.
[19] LIU Z, LI J G, SHEN Z Q, et al. Learning efficient convolutional networks through network slimming[C] / / 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2755-2763.
[20] 陈建强, 刘明宇, 符秦沈, 等. 基于深度学习的热轧钢带表面缺陷检测方法 [ J] . 自动化与信息工程, 2019, 40(4) : 11-16, 19. CHEN J Q, LIU M Y, FU Q S, et al. Hot rolled steel strip surface defect detection method based on deep learning [ J] . Automation & information engineering, 2019, 40(4) : 11-16, 19.

Similar References:

Memo

Last Update: 2022-08-20