FR-YOLO: A Focus-and-Reconstruct Mechanism for Lightweight Small Object Detection in Drone Imagery

Automatic detection of small objects in UAV imagery is a challenging problem that is of great interest in aerial surveillance and intelligent transport. The detection model must cope well with feature degradation of tiny targets, drastic scale variations, and strict constraints on onboard com-putational resources. This paper proposes a lightweight attention-based network, called FR-YOLO, to address the "focus" and "reconstruct" chal-lenges in small object detection. We introduce two novel components: the Local Feature Enhancement (LFE) module to precisely suppress back-ground noise via spatial attention, and the Content-aware Feature Reassem-bly (CFR) module to rectify spatial feature misalignment and recover fine-grained details during upsampling. The proposed modules are seamlessly integrated into the YOLO11n backbone. Extensive experiments on the au-thoritative VisDrone2019 benchmark indicate that FR-YOLO outperforms the YOLO11n baseline by a significant margin of 1.5% and surpasses the widely adopted YOLOv8n by 0.7% (achieving 36.4% mAP), all while maintaining a 16% smaller model size (2.53M parameters) compared to YOLOv8n. We have made the code available to the public at: https://github.com/Liu999hongwei/FR-YOLO.