| Citation: | LI Chenghao, RU Le, HE Linyuan, et al. Target detection method for region proposal network with variable anchor box[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1610-1617. doi: 10.13700/j.bh.1001-5965.2019.0531(in Chinese)
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Object detection is a hot topic in the field of computer vision. At present, object detection methods based on deep learning can be divided into two categories: two-stage detection and one-stage detection. The former has higher accuracy, while the latter has better speed. In order to improve the performance of detection, anchor mechanism is introduced in both categories. In this paper, Attention Guidance (AG) module is introduced in the two-stage detection method based on the deep convolutional neural network, which guides the anchor mechanism of Region Proposal Network (RPN), making the selection of preselected box shape more diversified. At the same time, to solve the problem of false detection and missed detection in the traditional post-processing Non-Maximum Suppression (NMS) algorithm, a Confidence factor NMS (Cf-NMS) method is proposed, which makes a great contribution to the overall performance of the model. Experiment results showed that, although it has a slight decrease in speed performance, the proposed method has an improvement in accuracy in both the RPN variant and the existing advanced method.
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