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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Detection of vehicle parts based on Faster R-CNN and relative position information
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Detection of vehicle parts based on Faster R-CNN and relative position information

机译：基于Faster R-CNN和相对位置信息的车辆零件检测

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摘要

Detection and recognition of vehicles are two essential tasks in intelligent transportation system (ITS). Currently, a prevalent method is to detect vehicle body, logo or license plate at first, and then recognize them. So the detection task is the most basic, but also the most important work. Besides the logo and license plate, some other parts, such as vehicle face, lamp, windshield and rearview mirror, are also key parts which can reflect the characteristics of vehicle and be used to improve the accuracy of recognition task. In this paper, the detection of vehicle parts is studied, and the work is novel. We choose Faster R-CNN as the basic algorithm, and take the local area of an image where vehicle body locates as input, then can get multiple bounding boxes with their own scores. If the box with maximum score is chosen as final result directly, it is often not the best one, especially for small objects. This paper presents a method which corrects original score with relative position information between two parts. Then we choose the box with maximum comprehensive score as the final result. Compared with original output strategy, the proposed method performs better.
机译：车辆的检测和识别是智能交通系统（ITS）中的两项基本任务。当前，一种普遍的方法是首先检测车身，徽标或车牌，然后对其进行识别。因此，检测任务是最基本的，也是最重要的工作。除徽标和车牌外，其他一些部件（如车脸，灯，挡风玻璃和后视镜）也是能够反映车辆特性并用于提高识别任务准确性的关键部件。本文对汽车零部件的检测进行了研究，其工作是新颖的。我们选择Faster R-CNN作为基本算法，并以车身所在图像的局部区域作为输入，然后可以得到具有自己分数的多个边界框。如果直接将具有最高分的盒子选为最终结果，则通常不是最好的盒子，尤其是对于小物件。本文提出了一种利用两部分之间的相对位置信息校正原始分数的方法。然后，我们选择综合得分最高的框作为最终结果。与原始输出策略相比，该方法具有更好的性能。

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