Lightweight Network-Based Surface Defect Detection Method for Steel Plates

Changqing Wang, Maoxuan Sun, Yuan Cao (), Kunyu He, Bei Zhang, Zhonghao Cao and Meng Wang
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Changqing Wang: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Maoxuan Sun: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Yuan Cao: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Kunyu He: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Bei Zhang: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Zhonghao Cao: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
Meng Wang: College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China

Sustainability, 2023, vol. 15, issue 4, 1-12

Abstract: 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.

Keywords: defect detection; lightweight; cavity spatial convolution; spatial attention (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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