用摄像头+CNN识别卡车抬轴，实时准确率高。
Camera-based method for the detection of lifted truck axles using convolutional neural networks
- 基于YOLOv8s的图像检测模型，从垂直车流视角捕捉卡车图像。
- 精确率达87%，召回率达91.7%，推理仅需1.4毫秒。
- 适合交通执法系统部署，尤其针对抬轴车辆检测难题。
车辆识别与分类在交通管控系统中至关重要。现有称重动态（WIM）系统虽能分类多数车型，却难以准确识别抬轴卡车。目前针对抬轴检测的商用与技术方法极为稀缺。本文作为欧洲项目SETO（智能交通执法）的一部分，提出一种基于卷积神经网络（CNN）的方法，采用YOLOv8s模型，通过垂直于车流方向的摄像头拍摄的卡车图像，实现抬轴检测。评估结果显示，该方法精度为87%，召回率为91.7%，推理时间仅为1.4毫秒，具备实时部署潜力。研究建议未来可通过扩大数据集规模和应用图像增强方法进一步提升性能。
原文摘要 · Abstract (English)
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.
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