关键词: 深度学习, 多尺度多人目标检测, Faster R-CNN网络, FPN网络, RPN网络, 锚点

Abstract:

Deep learning has the advantages of autonomous learning object features, high recognition rate and strong robustness. Current human detection methods based on deep learning cannot adapt to changing scale of the target effectively. To solve this problem, this paper proposes a multi-scale and multi-human detection method, by combining the FPN feature pyramid with the two stages of the Faster R-CNN network respectively. At the same time, the number of positive and negative anchor points generated in RPN phase is balanced, and a more suitable aspect ratio of anchor points is adopted to optimize the original network. The results of experiments based on standard datasets PETS 2009, Caltech and INRIA show that, the proposed detection method performs better than the mainstream deep learning object detection algorithm.

Key words: deep learning, multi-scale and multi-human detection, Faster R-CNN network, FPN network, RPN network, anchor