Small-Scale Pedestrian Detection Based on Improved Faster R-CNN
摘要
To improve the accuracy of small-scale pedestrian detection,this paper proposes a target detection method based on improved Faster R-CNN.The network structure uses a new aligned pooling layer based on bilinear interpolation to avoid the positional deviation caused by two quantization operations in Region of Interest(ROI)pooling.Then a cascade-based multi-layer feature fusion strategy is designed,which concatenates shallow feature maps with rich detail information and deep feature maps with abstract semantic information to address the insufficiency of feature information of small-scale pedestrians in deep feature maps.Experimental results on INRIA and PASCAL VOC2012 datasets show that the proposed method increases the mean Average Precision(mAP) by 17.58% and 23.78% respectively compared with detection method based on Faster R-CNN with the same efficiency of small-scale pedestrian detection.