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The image sets always contains most of the simple samples and a small number of difficult samples. If the network can automatically select difficult samples, the performance will be improved. In the field of object detection, R-CNN, Fast-RCNN (FRCN) and Faster R-CNN (FRCNN) are regarded as the leader of the detection network architectures. They focus on solving the time efficiency problem, but did not automatically choose difficult samples. The recent Online Hard Examples Mining (OHEM) method is a good solution to this problem. But for small sample sets, the effectiveness is not good enough. In this paper, the feature extraction layer as well as the difficult mining method are designed and applied on Faster-RCNN detector. The network proposed here achieved 78.3% mAP on the PASCAL VOC2007 test set, which are 5.1 point higher than that of FRCNN (VOC2007, 73.2%).

Training Deep Residual Network with Funnel Online Hard Examples Mining Method on Object Detector