A more efficient method for wire rope surface defect detection based on fusing cross-stage features and lightweight convolution modules

Abstract Detecting surface defects in industrial production presents challenges, including deploying lightweight algorithms on edge devices and balance between detection speed and accuracy. This paper introduces a novel lightweight method YOLO-FP for real-time detection of wire rope defects in industrial environments. Utilizing the YOLOv7tiny framework, we developed a lightweight cross-stage feature fusion module to enhance processing feature information. This mitigates the impact of redundant information from traditional convolution, reducing network size and improving detection speed. Additionally, the YOLO-FP network integrates lightweight convolution modules and an attention mechanism. Trained and tested on data from Changan, Great Wall, and Guangzhou Automobile models, our method achieved a 96.06% mean average precision, surpassing the original YOLOv7tiny model. Furthermore, it reduces the model size by 41.09% and enhances detection speed by 18.53%, making it promising for real-time edge device applications in wire rope production.