Steel surface defect detection based on deformable convolution feature fusion

Minghui Wang; Chao Yin; Zipeng Zhang

doi:10.1117/12.3035009

11 July 2024 Steel surface defect detection based on deformable convolution feature fusion

Minghui Wang, Chao Yin, Zipeng Zhang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102Z (2024) https://doi.org/10.1117/12.3035009
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

In steel production, the quantity of surface defects is a key indicator. Traditional detection methods mainly use flash frequency to detect defects, but this method has issues such as slow speed and low accuracy. Therefore, it is of great research significance to improve the detection speed and accuracy of surface defects in steel. The main focus of this paper is to explore the task of detecting surface defects in steel, and we will first improve the YOLOv8 model to enhance its performance in detecting surface defects on steel. We will optimize the loss function calculation method, neck end, and backbone network. The specific improvements are as follows: To address issues such as dense distribution and easy overlap of annotation boxes for steel surface defect targets, we introduce deformable convolution modules into the backbone network of YOLOv8. By adding deformable convolution modules, we can flexibly handle situations where detection point receptive fields are insufficient and enhance attention towards detection targets. Secondly, replacing PANet feature pyramid with BiFPN, network allows for more effective capture of target boundary information and improves detection efficiency through multi-scale semantic feature fusion. Finally, in order to accelerate model convergence speed and improve regression accuracy when improving the YOLOv8 model, we decide to replace the CIoU regression loss function used in the original model with EIoU (Efficient Intersection over Union) loss function.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minghui Wang, Chao Yin, and Zipeng Zhang "Steel surface defect detection based on deformable convolution feature fusion", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102Z (11 July 2024); https://doi.org/10.1117/12.3035009

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KEYWORDS

Convolution

Object detection

Deformation

Detection and tracking algorithms

Defect detection

Data modeling

Target detection

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