期刊论文详细信息
IEEE Access
An IF-RCNN Algorithm for Pedestrian Detection in Pedestrian Tunnels
Changliu Niu1  Jun Han1  Jin Ren1 
[1] School of Information Science and Technology, North China University of Technology, Beijing, China;
关键词: Pedestrian detection;    deep learning;    faster RCNN;    feature fusion;   
DOI  :  10.1109/ACCESS.2020.3022517
来源: DOAJ
【 摘 要 】

In order to deal with the problems of background mixing, pedestrian blur and pedestrian multi-scale in pedestrian tunnels, we proposed an improved faster region based convolution neural network (IF-RCNN) pedestrian detection method, which uses deep CNN to automatically extract features from pictures instead of traditional manual design features. In this paper, an improved region proposal network (RPN) structure is proposed to solve the multi-scale problem of pedestrians in tunnel. The anchor size in RPN network is further improved in the face of pedestrian images in tunnels with small total pixels. Meanwhile, feature fusion technology is introduced to the algorithm to output the features of different convolution layers. The image is fused to enhance the detection performance of blurred and occluded pedestrians in tunnel. Experimental results show that IF-RCNN algorithm has better detection performance in tunnel data set and VOC2007 data set.

【 授权许可】

Unknown   

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