Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit that the UAV could fundamentally improve detection performance by changing its viewpoint. Active Object Detection (AOD) offers an effective way to achieve this purpose. Through Deep Reinforcement Learning (DRL), AOD endows the UAV with the ability of autonomous path planning to search for the observation that is more conducive to target identification. Unfortunately, there exists no available dataset for developing the UAV AOD method. To fill this gap, we released a UAV's eye view active vision dataset named UEVAVD and hope it can facilitate research on the UAV AOD problem. Additionally, we improve the existing DRL-based AOD method by incorporating the inductive bias when learning the state representation. First, due to the partial observability, we use the gated recurrent unit to extract state representations from the observation sequence instead of the single-view observation. Second, we pre-decompose the scene with the Segment Anything Model (SAM) and filter out the irrelevant information with the derived masks. With these practices, the agent could learn an active viewing policy with better generalization capability. The effectiveness of our innovations is validated by the experiments on the UEVAVD dataset. Our dataset will soon be available at https://github.com/Leo000ooo/UEVAVD_dataset.

本研究针对无人机（UAV）基于物体检测中的遮挡问题，提出了一种新方法。通过引入深度强化学习和自主动路径规划，增强无人机识别目标的能力。同时，我们发布了名为UEVAVD的数据集，以支持UAV主动物体检测的研究，并通过实验验证了改进方法的有效性。

UEVAVD：用于开发无人机视角主动物体检测的数据集