Low-altitude target detection algorithm for intelligent scenic areas based on improved YOLOv10

With the development of drone technology, its application in intelligent scenic areas provides a new solution for tourist flow monitoring. To enhance detection accuracy and satisfy real-time demands, this study proposed a low-altitude target detection algorithm of intelligent scenic areas based on improved YOLOv10, and developed an intelligence scenic areas tourist flow monitoring and statistic system accordingly. By introducing the Large Separable Kernel Attention (LSKA) mechanism, the algorithm optimizes the Spatial Pyramid Pooling Fast (SPPF) module and effectively capturing long-range dependencies in images. In addition, we added a Small Target Detection Layer(STDL) to the YOLOv10 network structure to retain more location information and detailed features about small targets. Results from experiments conducted on the VisDrone2019 dataset show that, compared to the original YOLOv10 model, the enhanced version demonstrates an improvement in Recall by 2.0% and an increase in mAP@0.5 by 1.7%. Compared with other mainstream models, our proposed algorithm has improved on many evaluation metrics, and fulfills the requirements for real-time detection. It has been successfully applied to Tsingtao Beer Museum and has achieved good results. The results of the experiments indicate that the algorithm performs well in detecting low-altitude aerial photography images of drones, and provides effective technical assistance for the safety management of intelligent scenic areas.