The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

中文翻译：

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基于改进的Faster R-CNN模型的图像车型识别方法

车辆数量的快速增加导致交通拥堵、交通事故和机动车犯罪率不断上升。各类停车场的管理也变得越来越具有挑战性。车辆类型识别技术可以减少人类在车辆管理操作中的工作量。因此，应用图像技术进行车型识别对于综合交通管理具有重要意义。本文提出了一种改进的具有卷积神经网络特征的更快区域（Faster R-CNN）模型用于车辆类型识别。首先，结合不同卷积层的输出特征来提高识别精度。然后，通过原始图像的上下文特征和对象边界框优化策略来提高识别模型的平均精度（AP）。最后，对比实验使用了轿车、运动型多用途车（SUV）和货车三种车辆类型的车辆图像数据集。实验结果表明，改进的识别模型能够有效识别图像中的车辆类型。三种车型的AP分别为83.2%、79.2%和78.4%，平均精度（mAP）比传统Faster R-CNN模型高1.7%。