Lightweight detection network for arbitrary-oriented vehicles in UAV imagery via precise positional information encoding and bidirectional feature fusion.

Unmanned aerial vehicles (UAVs) open up new opportunities for transportation monitoring. However, the vehicle targets in UAV images are situated in dynamic scenarios, such as uncertain background, dramatically varying arrangement density, multi-scale, and arbitrary-oriented. Most strategies for UAV-based monitoring require complex manoeuvring and still lack accurate abilities and lightweight structures. Consequently, designing effective detection methods with both speed and accuracy is challenging. This paper proposes a lightweight YOLO-based arbitrary-oriented vehicle detector via precise positional information encoding and bidirectional feature fusion to address the above issues. First, an additional angular classification prediction branch is added to the YOLO head network to significantly improve the detection performance for arbitrary-oriented vehicles without incurring the extra computational complexity and burden. Second, a C3 module embedded coordinate attention (C3CA) is presented to capture long-range dependencies and preserve vehicles' precise positional information in feature maps. Then, a fully connected bidirectional feature fusion module (FC-BiFPN) is applied at the neck of the YOLO detection framework, which is helpful for multi-scale vehicle detection. This module can efficiently aggregate features at different resolutions and automatically enhance information interaction. Finally, experiments and comparisons on vehicle and remote sensing datasets demonstrate that our approach outperforms the state-of-the-art methods in balancing precision and efficiency. In addition, the overall network design follows the lightweight concept, which better meets the real-time requirements of the UAV urban traffic monitoring platform in realistic scenarios. [ABSTRACT FROM AUTHOR]