UAV Image Small Object Detection Algorithm Based on Multi-layer Feature Fusion and Attention Mechanism

Object detection in Unmanned Aerial Vehicle (UAV) aerial photography images is prone to incorrect or missed detections when the target is small, obstructed, or characterized by dense scales. To address the above challenges, this paper proposes the SNA-YOLOv5s algorithm for small target detection, which is based on YOLOv5s. First, the strided convolution layer in the original model is replaced with the Spatial Depth Transformation Convolution (SPD-Conv) module, eliminating the problem of detail loss caused by strided convolution operations and enhancing the model's ability to extract features from small objects. Second, a novel Average Pyramid Pooling-Fast (AGSPPF) module is designed, and an average pooling operation layer is introduced to address the issue of information loss that occurs while extracting feature information, thereby improving the model's feature extraction capability. Third, a new large-scale detection branch specifically for small targets is added to capture rich details in shallow features and enhance the detection capability for small targets. Finally, the Normalized Attention Mechanism (NAM) is embedded in the backbone network, where feature information is weighted to suppress invalid feature information. The proposed algorithm is trained and tested on the VisDrone2019 and NWPU VHR-10 datasets, on which it achieves mean Average Precision (mAP) of 42.3% and 96.5%, respectively, which is 8.4 and 2.6 percentage points higher than that of the baseline YOLOv5s model. The robustness and accuracy of the proposed model are validated by comparisons with other mainstream deep learning models.