A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization

TL;DR

This paper introduces a lightweight deep learning framework for steel surface defect detection, combining YOLOv9s with modules that enhance feature extraction, reduce redundancy, and improve detection accuracy in complex industrial environments.

Contribution

The study develops a novel defect detection model integrating C3Ghost, SCConv, and CARAFE modules with YOLOv9s, improving accuracy and efficiency over traditional methods.

Findings

Higher detection accuracy compared to existing methods

Enhanced robustness in complex environments

Efficient feature extraction and processing

Abstract

Surface defect detection of steel, especially the recognition of multi-scale defects, has always been a major challenge in industrial manufacturing. Steel surfaces not only have defects of various sizes and shapes, which limit the accuracy of traditional image processing and detection methods in complex environments. However, traditional defect detection methods face issues of insufficient accuracy and high miss-detection rates when dealing with small target defects. To address this issue, this study proposes a detection framework based on deep learning, specifically YOLOv9s, combined with the C3Ghost module, SCConv module, and CARAFE upsampling operator, to improve detection accuracy and model performance. First, the SCConv module is used to reduce feature redundancy and optimize feature representation by reconstructing the spatial and channel dimensions. Second, the C3Ghost module is introduced to enhance the model's feature extraction ability by reducing redundant computations and parameter volume, thereby improving model efficiency. Finally, the CARAFE upsampling operator, which can more finely reorganize feature maps in a content-aware manner, optimizes the upsampling process and ensures detailed restoration of high-resolution defect regions. Experimental results demonstrate that the proposed model achieves higher accuracy and robustness in steel surface defect detection tasks compared to other methods, effectively addressing defect detection problems.