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.

翻译：卡车车轴数量对车辆分类及道路系统运营至关重要，其用于确定服务费用及评估对路面结构的影响。虽然传统方法（如人工计数）可实现车轴统计，但利用深度学习与计算机视觉技术进行车轴计数的可行性正日益增强。本文旨在对比三种深度学习目标检测算法——YOLO、Faster R-CNN和SSD——在卡车车轴检测中的性能。通过构建数据集为神经网络提供训练与测试样本，并在不同基础模型上进行训练以提升训练效率并比较结果。我们基于五项指标评估实验结果：精确率、召回率、平均精度均值(mAP)、F1分数及每秒帧数(FPS)。结果表明，YOLO与SSD在准确性与性能方面表现相近，两者mAP均超过96%。数据集与代码均已公开提供下载。