| Citation: | Guoyu ZUO, Tingting DU, Lei MA, Jiahao LU, Daoxiong Gong. Region Proposal Generation for Object Detection Using Tree-DDQN by Action Attention[J]. Journal of Electronics & Information Technology, 2019, 41(3): 666-673. doi: 10.11999/JEIT180358
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Considering the problem of object detection of robots in the home environments, a Tree-Double Deep Q Network (TDDQN) based on the attention action strategy is proposed to determine the locations of region proposals. It combines DDQN with hierarchical tree structure. First, DDQN is used to select the best action of current state and obtain the right region proposal with a few actions executed. According to the state obtained after executing the selected action, the above process is repeated to create multiple "best" paths of the hierarchical tree structure. The best region proposal is selected using non-maximum suppression on region proposals that meet the conditions. Experimental results on Pascal VOC2007 and Pascal VOC2012 show that the proposed method based on TDDQN has better detection performance than other methods for region proposals of different numbers, different Intersection-over-Union (IoU) values and objects of different sizes and kinds, respectively.
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