WTM-YOLOv11: A Small Target Detection Model for Complex Scenes in UAV Applications

Abstract

Drones serve as critical tools for urban security surveillance and emergency response. However, aerial object detection faces inherent challenges, as key targets such as pedestrians and vehicles often appear tiny, densely distributed, and heavily occluded. These issues reduce detection accuracy and hinder real-world security deployment. To tackle these challenges, this paper proposes WTM-YOLOv11, an improved detection model for aerial surveillance scenarios. The core improvements are threefold: 1) the WIoUv3 loss function is adopted to optimize bounding box regression and enhance the model’s adaptability to complex sample distributions; 2) we integrate a C3k2-WTConv unit (by embedding WTConv into C3k2) into the backbone to enlarge the receptive field and strengthen multi-scale feature extraction; 3) the neck network is optimized to enhance cross-scale feature fusion and small-object representation capability. Comprehensive experiments demonstrate that WTM-YOLOv11 outperforms the vanilla YOLOv11 baseline, achieving a 2.3% improvement in mean average precision (mAP) while retaining a lightweight parameter footprint. The proposed method effectively enhances the detection performance of small and occluded targets in drone aerial imagery, providing a robust and feasible solution for intelligent public security surveillance systems.