Remote Sensing Tailing Pond Image–Detection Method Based on YOLOv8-RVSW

This study proposes an automated tailings pond-detection method based on the YOLOv8-RVSW model to address the limitations of traditional surveys. A tailings pond dataset was created using high-resolution satellite images, and data quality was improved through data augmentation techniques. In the model, YOLOv8's backbone feature network was replaced with RepViT to effectively capture global and local information. Additionally, the C2f module was enhanced to QuadraSE_C2f to focus on essential feature channels, and WIoUv3 was used as the loss function to improve object localization and detection accuracy. Experimental results indicate that compared with the original model, accuracy increased by 3.9% to 97.4%, recall improved by 4.9% to 96.3%, and mean average precision rose by 2.4% to 98.5%. This method significantly enhances the automation and intelligence of tailings pond monitoring, providing an effective tool for emergency monitoring.