EAFE‐OSOD: Efficient Attention Fusion Encoder for Open‐Set Object Detection in UAV Applications

Unmanned aerial vehicle (UAV) technology has achieved widespread applications. Object detection plays a crucial role in these applications. However, UAV‐based object detection faces three primary challenges: small object detection with complex backgrounds, novel category detection in open‐world scenarios and computational efficiency under resource constraints. Existing methods typically address these challenges individually, lacking unified solutions for resource‐constrained aerial platforms. We propose EAFE‐OSOD (efficient attention fusion encoder for open‐set object detection), an end‐to‐end framework that simultaneously tackles all three challenges through: (1) class‐agnostic mask embedding that guides the model to focus on specific regions, especially small objects; (2) CLIP text encoder integration for open‐set detection capabilities and (3) efficient fusion architectures that maintain computational efficiency through streamlined multi‐scale processing. Experiments on VisDroneZSD, VisDrone and UAVDT datasets demonstrate superior performance. On VisDrone, our method achieves 38.1% AP with 248.9G FLOPs, outperforming the second‐best method (36.6% AP and 658.7G FLOPs) while maintaining computational efficiency suitable for UAV platforms.