Lightweight Network-Based Surface Defect Detection Method for Steel Plates

被引:9
作者
Wang, Changqing [1 ,2 ,3 ]
Sun, Maoxuan [1 ,2 ,3 ]
Cao, Yuan [1 ,2 ,3 ]
He, Kunyu [1 ,2 ,3 ]
Zhang, Bei [1 ,2 ,3 ]
Cao, Zhonghao [1 ,2 ,3 ]
Wang, Meng [1 ,2 ,3 ]
机构
[1] Henan Normal Univ, Coll Elect & Elect Engn, Xinxiang 453007, Peoples R China
[2] Henan Key Lab Optoelect Sensing Integrated Applica, Xinxiang 453007, Peoples R China
[3] Henan Engn Lab Addit Intelligent Mfg, Xinxiang 453007, Peoples R China
基金
中国国家自然科学基金;
关键词
defect detection; lightweight; cavity spatial convolution; spatial attention;
D O I
10.3390/su15043733
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone network. This model improves the fusion of high- and low-level semantic information by designing feature pyramid networks with embedded spatial attention. According to the experimental findings, the suggested detection algorithm enhances the mapped value by about 4% once compared to the YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm, and the detection capability of steel surface defects is significantly enhanced to meet the needs of real-time detection of realistic scenes in the mobile terminal.
引用
收藏
页数:12
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