关键词: 深度学习, Faster RCNN, 行人检测

Abstract: In order to solve the problems of harsh underground environment, poor lighting, mixed background and multi-scale pedestrian, this paper proposes a pedestrian detection method of underground coal mine based on improved Faster RCNN. Deep convolutional neural network can replace traditional manual design feature to extract features automatically from images. Based on the Faster RCNN algorithm, RPN（Region Proposals Network） structure is improved and a “pyramid RPN” structure is proposed to solve multi-scale detection problem of pedestrian underground. At the same time, by adding feature fusion technology, the feature maps of different convolution layers are merged to improve the detetion performance for under-mine blur, occlusion and tiny pedestrian. The experimental results indicate that the improved Faster RCNN can effectively solve the pedestrian detection problem of underground coal mine, which obtains 90% detection accurary on the under-mine pedestrian dataset. The improved Faster RCNN algorithm is validated in the VOC 07 benchmark.

Key words: deep learning, Faster RCNN, pedestrian detection