Flying Vehicle Detection Under Complex Conditions With RGB–Infrared Imagery: A Large-Scale Open-Source Suite and Benchmark Approach

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Abstract
While coordination among multiple flying vehicles improves aerial logistics efficiency, safe and orderly operation requires robust detection and collision-avoidance capabilities. These requirements apply to passenger aircraft as well as to urban traffic and maritime environments, including emerging underwater flying vehicles. However, existing detection methods often fail under challenging conditions such as low illumination or cluttered backgrounds. Their progress is further constrained by the lack of large-scale benchmarks and the high computational and memory costs required to achieve high accuracy, which limits their deployment in resource-constrained scenarios, such as air-to-air collision avoidance in aerial vehicles. To address this gap, we introduce FT55k, an open-source benchmark comprising over 55,000 annotated RGB and infrared images across diverse environments. We further provide baseline approaches tailored for platforms with different computational demands. Extensive experiments on FT55k and three public datasets demonstrate the superior accuracy and efficiency of our methods compared with state-of-the-art approaches. Notably, our approach is the first flying vehicle detection method with a computational cost below 0.5 BFLOPs, achieving real-time performance at 62.3 FPS on an edge-computing device. This work presents the first comprehensive benchmark for flying vehicle detection in complex environments, establishing a practical and scalable foundation for future research and deployment in intelligent transportation safety. Our datasets is publicly accessible at https://github.com/chriszxk/Flying-Vehicle-Detection
Funding Information
  • Research Grants Council of Hong Kong, SAR (14217922)

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