Abstract: Object detection algorithm based on deep convolutional neural network has become a research hotspot in the field of object detection, which includes two-stage object detection algorithm based on region proposal and one-stage object detection algorithm based on position regression. Faster R-CNN is one of the typical algorithms for two-stage object detection. However, the imbalance between simple examples and hard examples in the training data set and the inter-class imbalance of sample data are important reasons that affect the detection accuracy of Faster R-CNN. In this paper, an improved algorithm of Faster R-CNN based on variable weight loss function and OHEM is proposed. Specifically, the Focal Loss function is introduced into the classification part of the network to adjust the inter-class imbalance of sample data and improve the imbalance of the number of simple examples and the number of hard examples by adjusting the weight. At the same time, the network structure is modified, and online hard example mining is introduced to further balance the number of simple samples and the number of hard samples so as to  improve the detection performance of the network. To verify the performance of the proposed algorithm, experiments on different data sets and different basic networks are conducted. The experimental results show that on the basic network VGG-16, the proposed algorithm improves the mAP by 09 percentage points on the Pascal VOC 2007 data set compared with the original algorithm and 1.7 percentage points on Pascal VOC 07+12 data set. On the basic network RES-101, the mAP of the proposed algorithm on Pascal VOC 2007 data set is 1.3 percentage points higher than that of the original algorithm, and the mAP of the proposed algorithm on Pascal VOC 07+12 data set is 1.5 percentage points higher.

Key words: deep learning, object detection, Focal Loss, online hard example mining