Dual Improvements DCNv4 and AsDDet Mechanisms for Small Object Detection in Aerial Imagery

This study introduces a dual enhancement mechanism integrating Deformable Convolution v4 (DCNv4) and an Asymmetric Decoupled Detection Head (AsDDet) to significantly improve the detection performance of small objects in high-altitude unmanned aerial vehicle (UAV) imagery using YOLOv11 model architecture. We embedded DCNv4 into YOLOv11 and redesigned its operational modality to establish an efficient and dynamic sparse operator while optimizing memory access processes. This integration enhances both the operational efficiency and overall performance. Furthermore, AsDDet, an efficient and innovative technique, employs an asymmetric dual-branch structure to decouple the classification and regression tasks. This allows the application of distinct convolutional layers tailored to process the positional and complex shape data characteristics of small objects. This dual-improvement design enables the model to accommodate the diverse demands of detection tasks better, thereby increasing the overall detection accuracy. An experimental validation was conducted using a dataset of urban appearance violations, which demonstrated significant improvements in precision, recall, and mean average precision (mAP). Specifically, the precision increased from 2.50% to 84.0%, recall improved from 3.94% to 81.3%, and mAP@0.5, and mAP@0.5:0.95, increased from 2.62% and 4.08% to 87.8% and 61.2%, respectively. Compared with the original YOLOv11 model and other mainstream object detection models, the proposed method exhibited marked enhancements in detection precision, recall, and mAP while effectively reducing both false negatives and false positives. The experimental results substantiate the significant performance gains achieved in UAV-based small-object detection, offering a novel solution for addressing related challenges in the field.