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首页> 外文会议>SPIE Conference on Multispectral Image Processing and Pattern Recognition >Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point
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Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point

机译：车辆零件检测基于更快的 - RCNN与车辆部件的位置约束

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Vehicle parts detection plays an important role in public transportation safety and mobility. The detection of vehicle parts is to detect the position of each vehicle part. We propose a new approach by combining Faster RCNN and three level cascaded convolutional neural network (DCNN). The output of Faster RCNN is a series of bounding boxes with coordinate information, from which we can locate vehicle parts. DCNN can precisely predict feature point position, which is the center of vehicle part. We design an output strategy by combining these two results. There are two advantages for this. The quality of the bounding boxes are greatly improved, which means vehicle parts feature point position can be located more precise. Meanwhile we preserve the position relationship between vehicle parts and effectively improve the validity and reliability of the result. By using our algorithm, the performance of the vehicle parts detection improve obviously compared with Faster RCNN.
机译：车辆零部件检测在公共交通安全和移动性中起着重要作用。车辆部件的检测是检测每个车辆部件的位置。我们通过组合更快的RCNN和三级级联卷积神经网络（DCNN）提出了一种新的方法。更快的RCNN输出是一系列具有坐标信息的边界框，我们可以从中找到车辆部件。 DCNN可以精确地预测特征点位置，即车辆部件的中心。我们通过组合这两个结果来设计输出策略。这有两个优点。边界箱的质量大大提高，这意味着车辆部件特征点位置可以更精确。同时我们保留了车辆部件之间的位置关系，有效地提高了结果的有效性和可靠性。通过使用我们的算法，与更快的RCNN相比，车辆部件检测的性能显然可提高。

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