Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO

In view of the complex environments and varying object scales in drone-captured imagery, a novel PARE-YOLO algorithm based on YOLOv8 for small object detection is proposed. This model enhances feature extraction and fusion across multiple scales through a restructured neck network. Additionally, it incorporates a lightweight detection head that is optimized for small objects, thereby significantly improving detection performance in cluttered and intricate backgrounds. To further enhance the extraction of small object features, the conventional C2f is replaced with a novel architecture. Moreover, the EMA-GIoU loss function is proposed to mitigate class imbalance and enhance robustness, particularly in scenarios characterized by skewed class distributions. Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. In addition, the PARE-YOLO model exhibits significant robustness, achieving a mean Average Precision (mAP) at a threshold of 0.5 values on the HIT-UAV dataset that are 0.8%, 0.5%, and 1.2% higher than those of YOLOv8, YOLOv10, and RT-DETR. These results underscore the effectiveness of PARE-YOLO in addressing the challenges inherent in aerial scenarios. The code will be available online (https://github.com/Sunnyxiao69/PARE-YOLO).