UEVAVD: A Dataset for Developing UAV's Eye View Active Object Detection

TL;DR

This paper introduces UEVAVD, a new UAV eye view active vision dataset, and enhances DRL-based active object detection by incorporating scene decomposition and sequence-based state representation, improving UAV detection performance.

Contribution

The paper provides the first UAV eye view active vision dataset and improves existing DRL-based AOD methods with scene decomposition and sequence modeling techniques.

Findings

The dataset facilitates UAV active object detection research.

Enhanced DRL method achieves better generalization in active viewing.

Experimental results validate the effectiveness of the proposed innovations.

Abstract

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.