A dataset of deep learning images of underground tectonic coal with five destruction types in Shaanxi, Shanxi and Hebei (2020–2021)

Machine vision recognition of underground coal destruction types can provide crucial information and technical support for predicting coal and gas herniation accidents, evaluating underground safety, and advancing the construction of intelligent mine engineering. At present, the discrimination of coal destruction types of tectonic coals in mines still relies on empirical macro-physical observations, which are heavily influenced by subjective experience and environmental conditions. To develop an effective machine learning model for classifying tectonic coal destruction types, it is essential to use high-quality, balanced, and realistic sample images of underground tectonic coal. Based on the photos taken by human-carried high-definition explosion-proof cameras in several different mine environments, this dataset provides a set of downhole native-state destructive coal images suitable for deep learning. The dataset size is about 1.55 G, including images of Non-destructive coal, Destructive coal, Strongly damaged coal, Pulverized coal and Fully pulverized coal under different mine conditions, illumination and angles. A total of 1,031 images, with an average of 206 images per class, can meet the basic training needs of deep learning models. During the data collection process, strict data quality control measures were taken, and the clarity and authenticity of the images were evaluated to ensure the reliability of the data. Designed for recognizing tectonic coal destruction types, this dataset is expected to provide essential resources for the future machine learning-based research on the recognition of coal destruction types.