Open-pit coal mine detection in large-scale remote sensing images

Satellite remote sensing is crucial for large-scale, regular monitoring of mineral mining. It helps grasp the mining status and fight illegal activities. However, challenges like scarce training datasets, low detection efficiency, fragmented targets, and inaccurate positioning exist. This paper focuses on open-pit coal mines. It presents a saliency-guided image-cutting algorithm and an improved non-maximum suppression-based object-relocation algorithm. These are integrated with a deep-learning object-detection model to form a deep-learning-based mine-detection framework. Tests on 10 large-scale images show the framework achieves 85.22% recall and 45.73% precision efficiently, outperforming pure deep-learning models.