8 March 2018 Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091J (2018) https://doi.org/10.1117/12.2287308
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
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.
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Liqin Yang, Nong Sang, Changxin Gao, "Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091J (8 March 2018); doi: 10.1117/12.2287308; https://doi.org/10.1117/12.2287308
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