UMSSS: A Visual Scene Semantic Segmentation Dataset for Underground Mines

Jiawen Wang, Chenfei Liao, Zhongqi Zhao, Lianghui Li, Xuan Gao, Suna Pan, Fangzhen Shi, Yudong Wang, Weijie Zhou, Kehu Yang

Abstract: Specialized datasets designed for mining scenarios are the essential foundation for the development, operation, and research of intelligent mines. Currently, the available datasets focus primarily on open-pit mines, with a lack of specialized datasets for underground mines. This gap severely hinders the application of intelligent solutions in underground mines. This paper proposes a challenging semantic segmentation dataset focusing on underground mines, named the underground mine scenes semantic segmentation (UMSSS) dataset, which contains 4200 high-quality annotated images and 18 annotated categories. To accurately capture the diversity and complexity of mine environments, we collect data from over ten mines located in various geographical regions. The UMSSS dataset is the first open-source semantic segmentation dataset for underground mines, widely covering varying lighting scenarios and diverse underground objects. The comparative experiments extensively explore the characteristics of the UMSSS dataset, providing a detailed evaluation of various state-of-the-art algorithms on the UMSSS dataset.