MFF‐YOLOv8: Small Object Detection Based on Multi‐Scale Feature Fusion for UAV Remote Sensing Images

ABSTRACT As a popular task in drone‐captured scenes, object detection involves images with a large number of small objects, but current networks often suffer missed and false detections. To address this problem, we propose a YOLO algorithm MFF‐YOLOv8 based on multi‐scale feature fusion for small target detection in UAV aerial images. First, a high‐fesolution feature fusion pyramid (HFFP) is designed, which utilizes high‐resolution feature maps containing much information about small objects to guide the feature fusion module, weighting and fusing feature maps to enhance the network's ability to represent small targets. Meanwhile, a reconstruction feature selection (RFS) module is employed to remove the large amounts of noise produced by high‐resolution feature maps. Second, a hybrid efficient multi‐scale attention (HEMA) mechanism is designed in the backbone network to maximize the retention and extraction of feature information related to small objects while simultaneously suppressing background noise interference. Finally, an Inner‐Wise IoU loss function (Inner‐WIoU) is designed for joint auxiliary bounding box and dynamic focal bounding box regression, which enhances the accuracy of network regression results, thus improving the detection precision of the model for small objects. MFF‐YOLOv8 was experimented on the VisDrone2019 dataset, achieving a 47.9% mAP50, 9.3% up compared with that of the baseline network YOLOv8s. Also, in order to verify the generalization of the overall network, it was evaluated on the DOTA and UAVDT datasets, and the mAP50 was improved by 3.7% and 1.8%, respectively. The results demonstrate that MFF‐YOLOv8 significantly enhances detection precision for small objects in UAV aerial scenes.