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.