Study on the Lightweighting of YOLOv5s Model for Precise Detection of Irregular-shaped Components

Abstract

Traditional methods for target recognition face challenges in meeting both precision and speed requirements for precision-shaped parts. In this paper, we propose an enhanced algorithm for precision-shaped part recognition by integrating deep learning theory. To achieve this, we modify the YOLOv5 network. Specifically, we replace the C3 module of the original network's backbone with the C3_ghostnetv2 network, which incorporates Ghostnetv2. This modification results in a lighter network with reduced model parameters and size, thereby improving detection speed. Moreover, we replace the convolution in the original network's neck with Deformable Convolution v2 (DCNv2) to enhance feature extraction for precision-shaped parts. We conduct comparative experiments on a self-made dataset of precision-shaped parts. The experimental results demonstrate that our improved algorithm reduces parameters by 13.7% and model size by 12.5% compared to the original YOLOv5s algorithm, while achieving a 1.4% increase in detection accuracy. The proposed algorithm accurately identifies and classifies precision-shaped machining parts, providing valuable technical support for subsequent intelligent production.