Truck Axle Detection with Convolutional Neural Networks

TL;DR

This paper compares three deep learning object detection algorithms—YOLO, Faster R-CNN, and SSD—for truck axle detection, demonstrating that YOLO and SSD achieve over 96% mAP with efficient performance.

Contribution

It provides a comparative analysis of deep learning models for truck axle detection, including dataset creation, training on various base models, and performance evaluation.

Findings

YOLO and SSD achieve over 96% mAP.

YOLO and SSD have similar accuracy and performance.

Datasets and code are publicly available.

Abstract

Axle count in trucks is important to the classification of vehicles and to the operation of road systems. It is used in the determination of service fees and in the impact on the pavement. Although axle count can be achieved with traditional methods, such as manual labor, it is increasingly possible to count axles using deep learning and computer vision methods. This paper aims to compare three deep-learning object detection algorithms, YOLO, Faster R-CNN, and SSD, for the detection of truck axles. A dataset was built to provide training and testing examples for the neural networks. The training was done on different base models, to increase training time efficiency and to compare results. We evaluated results based on five metrics: precision, recall, mAP, F1-score, and FPS count. Results indicate that YOLO and SSD have similar accuracy and performance, with more than 96\% mAP for both models. Datasets and codes are publicly available for download.