Automatic identification for vehicles is an important topic in the field of Intelligent Transportation Systems (ITS), and the vehicle logo is one of the most important characteristics of a vehicle. Therefore, vehicle logo detection and recognition are important research topics. Because of the problems that the area of a vehicle logo is too small to be detected and the dataset is too small to train for complex scenes, considering the speed of recognition and the robustness to complex scenes, we use deep learning methods which are based on data optimization for vehicle logo in complex scenes. We propose three augmentation strategies for vehicle logo data: cross-sliding segmentation method, small frame method, and Gaussian Distribution Segmentation method. For the problem of small sample size, we use cross-sliding segmentation method, which can effectively increase the amount of data without changing the aspect ratio of the original vehicle logo image. To expand the area of the logos in the images, we develop the small frame method which improves the detection results of the small area vehicle logos. In order to enrich the position diversity of vehicle logo in the image, we propose Gaussian Distribution Segmentation method, and the result shows that this method is very effective. The F1 value of our method in the YOLO framework is 0.7765, and the precision is greatly improved to 0.9295. In the Faster R-CNN framework, the F1 value of our method is 0.7799, which is also better than before. The results of experiments show that the above optimization methods can better represent the features of the vehicle logos than the traditional method, and the experimental results have been improved.

中文翻译：

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基于数据增强的复杂场景小样本车辆标志识别

车辆的自动识别是智能交通系统（ITS）领域的重要主题，并且车辆徽标是车辆最重要的特征之一。因此，车辆标志的检测和识别是重要的研究课题。由于车辆徽标的区域太小而无法检测到并且数据集太小而无法针对复杂场景进行训练的问题，考虑到识别速度和对复杂场景的鲁棒性，我们使用了基于复杂场景中车辆徽标的数据优化。我们提出了三种用于车辆徽标数据的扩充策略：交叉滑动分割方法，小框架方法和高斯分布分割方法。对于样本量小的问题，我们采用交叉滑动分割法，这样可以有效地增加数据量，而无需更改原始车辆徽标图像的长宽比。为了扩大图像中徽标的区域，我们开发了小框架方法，该方法改善了小面积车辆徽标的检测结果。为了丰富车辆徽标在图像中的位置多样性，提出了一种高斯分布分割方法，结果表明该方法非常有效。的 结果表明该方法非常有效。的 结果表明该方法非常有效。的我们的方法在YOLO框架中的F 1值为0.7765，并且精度大大提高到0.9295。在Faster R-CNN框架中，我们方法的F 1值为0.7799，也比以前更好。实验结果表明，上述优化方法比传统方法能更好地表现出车辆标志的特征，实验结果得到了改善。