Multiscale anchor box and optimized classification with faster R-CNN for object detection

Abstract

For the two-stage object detector as a faster region-convolutional neural network (Faster R-CNN), upgrading the accuracy of object recognition depends on the proposal box, which is generated by the region proposal algorithms. Due to the limitations of the anchor setting of Faster RCNN, the size of the proposal box generated by the region proposal network (RPN) used is large, which would easily cause a great number of overflows in the sliding search. To improve the accuracy of object detection and remit the overflow problem of the anchor box, multi-scale anchor box and moving overflow anchor box strategies are introduced here. Then, to increase the positive sample range of the foreground, the hierarchical weight cross entropy classification function is set for binary classification in the RPN network. These strategies could improve the accuracy of object detection. The experimental result achieves 76.2% AP on the Pascal VOC 2007(VOC 07) dataset, which is 2.7% higher than the Faster R-CNN. The result of the Pascal VOC 2012(VOC 12) test, we achieve 75.6% AP, is improved by 2.5% compared with the Faster R-CNN.