A lightweight model for multi-traffic object detection based on deep learning under complex traffic conditions

Guoqiang Chen; Yanan Cheng

doi:10.6180/jase.202206_25(3).0019

A lightweight model for multi-traffic object detection based on deep learning under complex traffic conditions

Guoqiang Chen This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Yanan Cheng¹

¹School of Mechanical and Power Engineering, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, Henan, China

Received: July 18, 2021
Accepted: September 17, 2021
Publication Date: October 25, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202206_25(3).0019

ABSTRACT

The object detection is extremely important in autonomous driving environment awareness. Besides vehicle and pedestrian detection, traffic signs and lights are important objects. The paper presents how to achieve precise results in multi-traffic object detection while minimizing the model size. A deep learning network YOLOv5s-Ghost-SE-DW is proposed based on the YOLOv5s. The proposed network can detect all traffic objects including traffic signs and lights. First, the convolution layer is replaced by Ghost module to reduce the parameter and model size. Second, in order to improve accuracy and real-time performance, the attention mechanism SELayer is embedded to fuse more spatial features. Third, the DW convolution is used to extract features and further reduce the parameter number. The effect of different modules on the whole network is verified by ablation experiments. The YOLOv5s-Ghost-SE-DW yields a model size of 5.22MB while achieving 15.58 FPS real-time performance on CPU. The FPS increases by 27.5%.

Keywords: ghost module; attention mechanism; DW convolution; real-time object detection; lightweight network; complex traffic conditions

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