Sky-YOLO: Advanced Small Object Detection for UAV Applications

Abstract

Object detection from a UAV perspective faces challenges such as object occlusion, unclear boundaries, and small target sizes, resulting in reduced detection accuracy. Additionally, traditional object detection algorithms have large parameter counts, making them unsuitable for resource-constrained edge devices. To address this issue, we propose a lightweight small-object detection algorithm: Sky-YOLO. Specifically, we introduce the MSFConv multi-scale feature map fusion convolution module into the backbone network to enhance feature extraction capability. The Neck part is replaced with the L-BiFPN module to reduce parameter count and strengthen feature fusion between layers. Additionally, based on the characteristics of UAV imagery, we incorporate the WIOU loss function, enabling efficient detection of blurred and occluded targets. Experimental results show that the Sky-YOLO model, with 60% fewer parameters than the original model, still achieves 39.7% accuracy on the VisDrone2019 validation dataset, a 6.7% improvement in accuracy over the original model.