Lightweight Vehicle Detection Based on Improved YOLOv5s

Abstract

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

“…Many scholars have conducted extensive and in-depth research in the field of lightweight networks and vehicle detection [ 17 , 18 , 19 ]. Chen et al [ 20 ] proposed an efficient detection network that achieves three times the detection speed of YOLOv3 by fusing the advantages of densely connected networks and separable convolutions.…”