In this paper, a real-world problem of the vehicle-type classification for automatic toll collection (ATC) is considered. This problem is very challenging because any loss of accuracy even of the order of 1% quickly turns into a significant economic loss. To deal with such a problem, many companies currently use optical sensors (OSs) and human observers to correct the classification errors. Herein, a novel vehicle classification method is proposed. It consists in regularizing the problem using one camera to obtain vehicle class probabilities using a set of convolutional neural networks (CNNs) and, then, uses the Gradient boosting-based classifier to fuse the continuous class probabilities with the discrete class labels obtained from the OS. The method is evaluated on a real-world dataset collected from the toll collection points of the VINCI Autoroutes French network. The results show that it performs significantly better than the existing ATC system and, hence, will vastly reduce the workload of human operators.

中文翻译：

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基于集成分类器的车型自动识别准确分类

在本文中，考虑了自动收费 (ATC) 车辆类型分类的实际问题。这个问题非常具有挑战性，因为即使是 1% 数量级的精度损失也会很快变成重大的经济损失。为了解决这样的问题，许多公司目前使用光学传感器（OS）和人类观察者来纠正分类错误。在此，提出了一种新的车辆分类方法。它包括使用一组卷积神经网络 (CNN) 对问题进行正则化，以获取车辆类别概率，然后使用基于梯度提升的分类器将连续类别概率与从操作系统获得的离散类别标签融合. 该方法是在从 VINCI Autoroutes 法国网络的收费站收集的真实世界数据集上进行评估的。结果表明，它的性能明显优于现有的 ATC 系统，因此将大大减少人工操作员的工作量。