Real-Time Small UAV Detection in Complex Airspace Using YOLOv11 with Residual Attention and High-Resolution Feature Enhancement

TL;DR

This paper introduces a new real-time system for detecting small drones in complex airspace using an improved YOLOv11 model with better accuracy and speed.

Contribution

The novel YOLOv11-ResCBAM framework combines residual attention and high-resolution features for improved small UAV detection.

Findings

The model achieves an mAP@0.5–0.95 of 0.845, a 7.9% improvement over the baseline.

It maintains real-time performance at 50.51 FPS while preserving spatial details for small-object detection.

Abstract

Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention Module (ResCBAM) and a high-resolution P2 detection head into the YOLOv11 architecture. ResCBAM enhances channel and spatial feature refinement while preserving original feature contexts through residual connections, and the P2 head maintains fine spatial details crucial for small-object localization. Evaluated on a custom dataset of 4917 images (11,733 after augmentation) across three classes (drone, bird, airplane), our model achieves a mean average precision at the 0.5–0.95 IoU threshold (mAP@0.5–0.95) of 0.845, representing a 7.9% improvement over the baseline YOLOv11n, while maintaining real-time inference at 50.51 FPS. Cross-dataset validation on VisDrone2019-DET and UAVDT benchmarks demonstrates promising generalization trends. This work demonstrates the effectiveness of the proposed approach for UAV surveillance systems, balancing detection accuracy with computational efficiency for deployment in security-critical environments.

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