Research On Pedestrian Detection Based On Regional Full Convolutional Network

Pedestrian detection is a research hotspot in image processing and computer vision and is widely used in autonomous driving,intelligent monitoring,and intelligent robots.Traditional pedestrian detection methods can achieve better pedestrian detection results under certain conditions,but there are problems with low detection accuracy in the context of low resolution and small pedestrian size.In response to the above problems,this paper introduces the object detection algorithm based on R-FCN in the deep learning method into pedestrian detection and makes some improvements based on R-FCN.Pedestrian detection research of convolutional network.Firstly,to make the pedestrian feature more accurate in the extraction process,a deformable convolutional layer is introduced in the Conv5 stage of the residual network to expand the receptive field of the feature map and improve the accuracy of feature extraction.Then,to improve the detection accuracy of small-sized pedestrians,a layer of position-sensitive scoring map is added after the Conv4 layer of the network,connected in parallel with the original position-sensitive scoring map detect pedestrians of different scales,which can effectively improve the detection accuracy of pedestrians.Finally,to solve the false detection phenomenon in pedestrian detection,the non-maximum suppression algorithm of the bootstrap strategy is used to replace the traditional non-maximum suppression algorithm,and the overlap rate and similarity measure of the two boxes are used to determine whether the two boxes are the same.Individuals,and setting thresholds to mine negative samples can effectively improve the problem of false detections in pedestrian detection.To verify the effectiveness of the improved R-FCN algorithm,comparative experiments were performed on the Caltech datasets and INRIA datasets with the original algorithm.Experimental results show that compared with the original algorithm,the pedestrian detection accuracy of this algorithm is improved by 12.1% and 11.54%,the missed detection rate is reduced by 9.69% and 5.02%,and the false detection rate is reduced by10.02% and 6.86%,respectively.The algorithm in this paper is compared with the representative single-stage detector SSD algorithm and the Faster R-CNN algorithm in the two-stage detector,and the detection accuracy is increased by 3.29% and 2.78%,respectively.The improved model achieves good performance and improves the accuracy of pedestrian detection.There are 44 figures,11 tables and 52 references in this paper.