An Efficient Multi-Level Visual Perception Framework for UAV-Based Tiny Object Detection and Tracking

Abstract

Although unmanned aerial vehicle (UAV) platforms provide flexible deployment and wide coverage for aerial surveillance, high-altitude dynamic viewpoints usually involve small-scale targets, dense object distributions, and frequent occlusions, which bring significant challenges to detection and tracking. To address these issues, this paper proposes an efficient multi-level visual perception framework for UAV-based joint object detection and tracking. At the detection level, the proposed method is built upon YOLOv8n, removes the original P5 branch for large-object detection, and introduces a high-resolution P2 detection head for small objects. The backbone is further redesigned in a lightweight manner, and a multi-level fusion strategy is introduced to strengthen the perception ability for small targets. At the tracking level, the state modeling in ByteTrack is improved by refining the Kalman filtering process, which enhances trajectory continuity and identity consistency under dynamic viewpoints. Experimental results on the aerial surveillance test site indicate that the proposed method significantly improves detection and tracking performance while achieving a favorable trade-off between accuracy and computational efficiency, providing a practical reference for subsequent applications in UAV-based target monitoring.