Enhanced YOLOv8 by integrating MSFF and MSAFM for UAV-Perspective Small Object Detection

Abstract

Detecting small objects is a formidable challenge in earth observation and remote sensing. To address the complexities associated with small object recognition in remote sensing imagery, this paper introduces the Multi-Scale Attention Feature Map (MSAFM) method. This approach effectively merges high-level and low-level feature maps by exploiting local features from the low-level maps and global features from the high-level maps, thereby substantially enhancing the fused feature map. Furthermore, we propose a feature map enhancement technique named Multi-Scale Feature Fusion (MSFF), which builds a feature pyramid within the same feature map using convolutional kernels of varying sizes and employs a bidirectional sampling strategy across different temporal sequences to augment the feature representation. When integrated into the YOLOv8 framework and evaluated on the VisDrone2019 dataset, these modules yielded notable improvements in accuracy, with mAP50 increasing by 11% and mAP50-95 by 7%, along with significant enhancements in detection accuracy across all categories.