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摘要: 为了模拟人眼的视觉注意机制, 快速、高效地搜索和定位图像目标, 提出了一种基于循环神经网络(Recurrent neural network, RNN)的联合回归深度强化学习目标定位模型. 该模型将历史观测信息与当前时刻的观测信息融合, 并做出综合分析, 以训练智能体快速定位目标, 并联合回归器对智能体所定位的目标包围框进行精细调整. 实验结果表明, 该模型能够在少数时间步内快速、准确地定位目标.Abstract: To simulate the visual attention mechanism of the human eye, search and locate image objection quickly and efficiently, this paper proposes a union regression deep reinforcement learning object localization model based on recurrent neural network (RNN), which fuses the historical observation information with the observation information at the current time, then makes a comprehensive analysis to train the agent to quickly locate the object, and combine with the regressor to fine-tune the object bounding box positioned by the agent. Experiments show that the proposed model can accurately and rapidly locate the object in a few time steps.
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图 4 融合网络
${f}_{c}\left({\theta }_{c}\right)$ Fig. 4 Integration network
$ {f}_{c}\left({\theta }_{c}\right) $ 
图 5 动作网络
$ {f}_{a}\left({\theta }_{a}\right) $ Fig. 5 Action network
$ {f}_{a}\left({\theta }_{a}\right) $ 
图 6 位置网络
$ {f}_{l}\left({\theta }_{l}\right) $ Fig. 6 Location network
$ {f}_{l}\left({\theta }_{l}\right) $ 
图 7 回归网络
$ {f}_{g}\left({\theta }_{g}\right) $ Fig. 7 Regression network
$ {f}_{g}\left({\theta }_{g}\right) $ 
图 19 回归器精调后IoU交叠区域示意图
Fig. 19 Schematic diagram of IoU overlapping area after fine adjustment of regressor
表 1 不同算法在VOC 2007测试集上的定位精度表现(节选部分种类)
Table 1 Positioning accuracy performance of different algorithms on VOC 2007 test set (category of excerpts)
算法 Aero Bike Bird Boat Bottle Bus Car Cat mAP Faster R-CNN 86.5 81.6 77.2 58.0 51.0 78.6 76.6 93.2 75.3 Caicedo 57.9 56.7 38.4 33.0 17.5 51.1 52.7 53.0 45.0 Bueno 56.1 52.0 42.2 38.4 22.1 46.7 42.2 52.6 44.0 UR-DRQN 59.4 58.7 44.6 36.1 28.3 55.3 48.4 52.4 47.9 
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表 2 不同算法平均每个轮次的定位耗时
Table 2 The average location time of each epoch in different algorithms
算法 Faster R-CNN Caicedo Bueno UR-DRQN 定位耗时 (s/轮次) 372 271 251 219
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