ST-YOLO: An Enhanced Detector of Small Objects in Unmanned Aerial Vehicle Imagery

This paper presents a redesigned YOLO-based model tailored for small-object detection in drone applications. To enhance its performance in detecting small and blurry targets, this study introduces the C3_CAA module to refine feature maps, integrates the CPA module and SI-IoU to improve detection accuracy, and incorporates channel and spatial attention mechanisms to further enhance target localization and identification performance.The experimental results indicated that the proposed method performs well on multiple datasets. The mAP value increases by 2% on the VISDRONE dataset, 1.6% on the UAVDT dataset, 0.9% on the CARPK dataset, and 1% on the UAVROD data set.