An improved lightweight object detection algorithm for YOLOv5

Hao Luo; Jiangshu Wei; Yuchao Wang; Jinrong Chen; Wujie Li

doi:10.7717/peerj-cs.1830

An improved lightweight object detection algorithm for YOLOv5

PeerJ Comput Sci. 2024 Jan 30:10:e1830. doi: 10.7717/peerj-cs.1830. eCollection 2024.

Authors

Hao Luo¹, Jiangshu Wei¹, Yuchao Wang², Jinrong Chen¹, Wujie Li¹

Affiliations

¹ College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
² College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Abstract

Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

Keywords: Attention mechanisms; Deep learning; Ghost module; Lightweight object detection; YOLOv5.

Grants and funding

This work was supported by the University Industry Collaborative Education Program (Grant No. 22097077265201). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.