Intelligent Identification of Coal Geographical Origin Based on Improved U-Net3+ and Near-Infrared Spectroscopy

The determination of coal’s geographical origin is pivotal for assessing coal quality and improving import and export inspections. Conventional methods for determining coal’s geographical origin, which primarily involve time-intensive, labor-intensive, and costly chemical experiments, necessitate optimization for greater efficiency. Near-Infrared Spectroscopy (NIRS) offers a viable solution with its accuracy, speed, and non-destructive nature in detecting chemical compositions, which has achieved success in various research fields. This study introduces a non-destructive technique that integrates NIRS with deep learning for identifying the geographical origin of coal, addressing the limitations of conventional approaches. In tackling the issue of abnormal Near-Infrared (NIR) data, the study employs a data cleaning method based on Euclidean distance to identify and eliminate outliers. Standard normal variate transformation is utilized to extract features from the NIR data in the experiment. Furthermore, we introduce an improved U-Net3+ model by integrating residual modules into the encoder and incorporating the concurrent spatial and channel squeeze & excitation attention block to enrich the model’s feature capturing ability. Experimental results demonstrate that the optimized U-Net3+ model attains an impressive 97.45% identification accuracy, outperforming other algorithms. The proposed method stands as a promising solution in the realm of coal quality assessment and inspection.