Policy Generalization Enhancement for UAV Active Object Detection via Divide-and-Conquer Sharpness-Aware Gradient Matching

Target detection in aerial images captured by unmanned aerial vehicles has long been hampered by occlusion. Active Object Detection (AOD) aims to fundamentally address this issue from the active vision perspective, typically realized through the Deep Reinforcement Learning (DRL) paradigm. However, the active observation policy often suffers from low generalization ability, thus limiting its practical application. In this paper, we propose Divide-and-Conquer Sharpness-Aware Gradient Matching (DC-SAGM), a novel sharpness-based Domain Generalization (DG) method, to effectively enhance the generalization capacity of the agent’s policy. Specifically, we train the agent to learn the active observation policy using the conventional DRL approach. Sharpness-Aware Gradient Matching (SAGM) is employed during training, improving the model’s generalization performance by minimizing the sharpness metric of the loss landscape. Nevertheless, the imperfect state representation and classifier preference in the AOD problem lead to fierce gradient conflicts, deteriorating the effectiveness of SAGM. We address this incompatibility by using a divide-and-conquer strategy and exclude gradient conflicts via the majority-rule gradient surgery operation. Extensive experimental results on the UEVAVD dataset validate DC-SAGM’s superiority in helping the agent’s policy achieve better generalization compared to extensive policy learning approaches.