YOLO‐Tiny: A lightweight small object detection algorithm for UAV aerial imagery

Abstract In unmanned aerial vehicle (UAV) aerial target detection tasks, two main challenges exist: the limited computational resources of UAV terminals, which are not conducive to running complex models, and the prevalence of small targets, which can easily lead to missed detections and false positives. To address these issues, this study proposes a lightweight and high‐accuracy small‐object detection algorithm for UAV aerial imagery that is based on YOLOv5s. First, the network structure is optimized by removing the layers in YOLOv5s primarily used for detecting large targets (P4 and P5) and adding layers primarily used for detecting small targets (P2 and P3). This enables the model to focus more on extracting small‐object features. A lightweight dynamic convolution is subsequently introduced in the C3 module, and the lightweight LW_C3 and LW_downsampling modules are designed for feature extraction and downsampling operations. This enhances the model's feature extraction capability while achieving a lightweight design. Finally, the adaptive multi‐scale spatial feature fusion (AMSFF) module is designed to adaptively learn the spatial weights of the feature maps at different levels, thereby further strengthening the effective fusion of multi‐scale features. Experimental results show that the improved YOLO‐Tiny model has higher accuracy and lower complexity, hence validating its excellent performance.