Camera-based method for the detection of lifted truck axles using convolutional neural networks
This addresses a specific gap in vehicle classification for transport enforcement, but it is incremental as it applies an existing CNN method to a new domain.
The paper tackled the problem of detecting lifted truck axles, which is difficult for existing weigh-in-motion systems, by proposing a CNN-based method using YOLOv8s on camera images, achieving a precision of 87% and recall of 91.7% with an inference time of 1.4 ms.
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.