论文标题
卷积神经网络检测卡车车轴检测
Truck Axle Detection with Convolutional Neural Networks
论文作者
论文摘要
卡车中的轴计数对于车辆的分类和道路系统的运行至关重要。它用于确定服务费和对人行道的影响。尽管可以使用传统方法（例如手动劳动）来实现轴计数，但使用深度学习和计算机视觉方法计数轴是越来越有可能实现的。本文旨在比较三种深度学习对象检测算法，Yolo，更快的R-CNN和SSD，以检测卡车轴。构建了一个数据集，以提供神经网络的培训和测试示例。培训是在不同的基本模型上进行的，以提高训练时间效率并比较结果。我们根据五个指标评估了结果：精度，召回，MAP，F1得分和FPS计数。结果表明，Yolo和SSD具有相似的精度和性能，这两种模型的MAP超过96 \％。数据集和代码可公开下载。
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.
