A lightweight car damage detection algorithm

Abstract

In response to challenges such as the large number of parameters and high computational demands of vehicle appearance damage detection models, which hinder deployment on mobile devices, this paper presents a study focusing on lightweight and high-precision optimization of the YOLOv5s target detection algorithm. Specifically, we introduce the lightweight network into the YOLOv5s architecture to create a more efficient network. Furthermore, we integrate the attention mechanism to enhance feature extraction capabilities and employ knowledge distillation to improve algorithm accuracy. These enhancements aim to boost target detection performance. The experimental results illustrate that our optimized YOLOv5 algorithm achieves significant improvements in both speed and accuracy on the car damage dataset.