Title: Research on heavy truck recognition algorithm based on deep learning

Authors: Huan Wang; Dun Zhang; Zhikai Huang

Addresses: Nanchang Institute of Technology, Nanchang, Jiangxi, China ' Nanchang Institute of Technology, Nanchang, Jiangxi, China ' Nanchang Institute of Technology, Nanchang, Jiangxi, China

Abstract: The Chinese economy has developed rapidly by taking advantage of convenient and rapid freight transport. Heavy trucks have always attracted much attention as the leading force in freight transportation. Although heavy trucks have significant advantages in freight transportation, their high emissions that cause air pollution and high accident rates have always been criticised. Monitoring of heavy trucks has been improving in China, and this paper adopts deep learning to identify heavy trucks to strengthen their supervision. Given the multi-target recognition problem in the actual scene, this paper uses a one-stage algorithm for target detection. The representative network SSD (Single Shot Multi-Box Detector) and YOLO (You Only Live Once) are compared. YOLO adopts the YOLOv5s structure, and the SSD network is subdivided into two networks by replacing the backbone structure. One backbone structure is VGG, and the other is Mobilenetv2. The final experimental results show that the SSD network with VGG as the backbone structure achieves the best Map value of 93.24%, which is 6.02% and 8.14% higher than the SSD and YOLOv5s training models with Mobilenetv2 backbone structure, respectively.

Keywords: convolutional neural network; deep learning; target detection; heavy truck recognition.

DOI: 10.1504/IJWMC.2022.126362

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.2, pp.132 - 138

Received: 12 Jan 2022
Accepted: 21 Mar 2022
Published online: 24 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article