基于双阈值-非极大值抑制的Faster R-CNN改进算法
摘要
According to the problems of target missed detection and repeated detection in the object detection algorithm, this paper proposes an improved Faster R-CNN algorithm based on dual threshold-non-maximum suppression. The algorithm first uses the deep convolutional network architecture to extract the multi-layer convolution features of the targets, and then proposes the dual threshold-non-maximum suppression (DT-NMS) algorithm in the RPN(region proposal network). The phase extracts the deep information of the target candidate regions, and finally uses the bilinear interpolation method to improve the nearest neighbor interpolation method in the original RoI pooling layer, so that the algorithm can more accurately locate the target on the detection dataset. The experimental results show that the DT-NMS algorithm effectively balances the relationship between the single-threshold algorithm and the target missed detection problem, and reduces the probability of repeated detection. Compared with the soft-NMS algorithm, the repeated detection rate of the DT-NMS algorithm in PASCAL VOC2007 is reduced by 2.4%, and the target error rate of multiple detection is reduced by 2%. Compared with the Faster R-CNN algorithm, the detection accuracy of this algorithm on the PASCAL VOC2007 is 74.7%, the performance is improved by 1.5%, and the performance on the MSCOCO dataset is improved by 1.4%. At the same time, the algorithm has a fast detection speed, reaching 16 FPS.