19 February 2018 A street rubbish detection algorithm based on Sift and RCNN
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Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 106080F (2018) https://doi.org/10.1117/12.2285043
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).
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XiPeng Yu, Zhong Chen, Shuo Zhang, Ting Zhang, "A street rubbish detection algorithm based on Sift and RCNN", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080F (19 February 2018); doi: 10.1117/12.2285043; https://doi.org/10.1117/12.2285043
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