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首页> 外文会议>International symposium 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：基于Faster-RCNN的车辆零件特征点位置约束的车辆零件检测。

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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：车辆零件检测在公共交通安全和出行中起着重要作用。车辆部件的检测是检测每个车辆部件的位置。我们提出了一种结合Faster RCNN和三级级联卷积神经网络（DCNN）的新方法。 Faster RCNN的输出是一系列带有坐标信息的边界框，从中可以找到车辆零件。 DCNN可以精确预测特征点位置，该特征点位置是车辆零件的中心。我们通过结合这两个结果来设计输出策略。这样做有两个优点。边界框的质量大大提高，这意味着可以更精确地定位汽车零件特征点的位置。同时，我们保留了汽车零件之间的位置关系，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Faster RCNN相比，车辆零件检测的性能明显提高。

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