A Steel Surface Defect Detection Model Based on YOLOv7-tiny

This article designs the PELAN structure based on the lightweight YOLOv7-tiny model for surface defect detection of hot-rolled steel strips. At the same time, the CA (Channel Attention) is embedded in the feature pyramid structure and the Slim-Neck structure is introduced at the neck. Through experimental result analysis, it is proved that the lightweight algorithm not only reduces model complexity, but also has better detection accuracy and processing speed. This article focuses on exploring the potential of lightweight YOLOv7-tiny models in steel surface defect detection. With the advancement of Industry 4.0, quickly and accurately detecting surface defects on steel strips has become crucial. However, traditional defect detection methods often face issues such as large computational requirements and poor real-time performance. Therefore, we have designed a novel PELAN structure that combines CA(Channel Attention)and Slim-Neck structure. The aim is to reduce model complexity while maintaining or even improving detection accuracy and processing speed. © (2024), (International Association of Engineers). All rights reserved.