Camera-based method for the detection of lifted truck axles using convolutional neural networks

TL;DR

This paper presents a CNN-based camera method for detecting lifted truck axles, achieving high accuracy and fast inference, addressing a gap in vehicle classification technology.

Contribution

Introduces a YOLOv8s-based approach for lifted axle detection in traffic images, improving real-time classification of modified trucks.

Findings

Precision of 87% in axle detection

Recall of 91.7% indicating high detection rate

Inference time of 1.4 ms suitable for real-time use

Abstract

The identification and classification of vehicles play a crucial role in various aspects of the control-sanction system. Current technologies such as weigh-in-motion (WIM) systems can classify most vehicle categories but they struggle to accurately classify vehicles with lifted axles. Moreover, very few commercial and technical methods exist for detecting lifted axles. In this paper, as part of the European project SETO (Smart Enforcement of Transport Operations), a method based on a convolutional neural network (CNN), namely YOLOv8s, was proposed for the detection of lifted truck axles in images of trucks captured by cameras placed perpendicular to the direction of traffic. The performance of the proposed method was assessed and it was found that it had a precision of 87%, a recall of 91.7%, and an inference time of 1.4 ms, which makes it well-suited for real time implantations. These results suggest that further improvements could be made, potentially by increasing the size of the datasets and/or by using various image augmentation methods.