RLPGB-Net: Reinforcement Learning of Feature Fusion and Global Context Boundary Attention for Infrared Dim Small Target Detection
Zhe Wang, Tao Zang, Zhiling Fu, Hai Yang, Wenli Du
Abstract
In infrared scenes, humans can easily observe objects in the scene with their eyes, even dim ones. To make the robot have the same visual ability, this paper proposes a pyramid-feature fusion target detection network, called RLPGB-Net, which combines reinforcement learning with aerial targets in the infrared scene. It makes use of the powerful decision-making ability of reinforcement learning to give corresponding weights to the extracted features and highlight the significant features of infrared dim small targets. In reinforcement learning, we use priori strategy guidance and long-term training methods to train weight-regulating agents. To eliminate the local influence on the detection results, such as bright interference points similar to the target, and to solve the problem of dim target detection effectively, the global context boundary attention module is introduced to eliminate the disadvantage of local comparison by using the global characteristics of different dimensions. At the same time, it can prevent the edge information of the refined target from being submerged in the background. Experimental results on SAITD and SIRST data sets show the effectiveness of the proposed method.