A Remote Sensing Small Object Detection Algorithm Based on Dynamic Snake Convolution and Attention Mechanism

As one of the key techniques in the field of machine learning, the technology of object detection has been widely applied in many aspects, particularly in remote sensing small object recognition. However, due to the fact that small objects in remote sensing images have high rotation, dense distribution, complex background, etc., the lack of small object feature extraction capability has become an urgent problem in the field of object detection. Therefore, this paper proposes a remote sensing small object detection algorithm YOLOv8-PNAD (YOLOv8-ParNet And Dynamic snake) based on Dynamic snake convolution and attention mechanism. Dynamic snake convolution and ParNet attention are incorporated in the backbone and neck networks to enhance the extraction capability of features at different scales. Multi-Group experiments are performed on the RSOD dataset, and the experimental outcomes reveal that the improved YOLOv8-PNAD network increases the mAP50 by 2.9% and the mAP50-95 by 1.8% compared to the original YOLOv8 network. By comparing with other mainstream models based on deep learning, it is found that the proposed algorithm improves 6.7% over the RA-BIFPN algorithm, which is also higher than several other existing state-of-the-art object detection algorithms, and demonstrates better recognition capabilities for small objects.