Multiclass Classification of Coal Gangue Under Different Light Sources and Illumination Intensities

As a solid mixture discharged during coal production, coal gangue possesses comprehensive utilization potential. Efficient sorting and pre-enrichment of its classification are crucial for green mining practices. This study categorizes coal gangue into four types—residual coal (RC), gray gangue (GG), red gangue (RG), and white gangue (WG)—based on their apparent color and utilization properties. The research systematically analyzed how different light sources and illumination intensities affect the visual characteristics of these gangue types. The results indicate that white light sources most accurately reproduce the real coloration and texture features of coal gangue, with optimal textural clarity achieved at moderate illumination levels. Different colored light sources selectively enhance spectral reflectance, and red light significantly improves RG recognition. Support vector machine (SVM)-based classification experiments demonstrate that white light sources achieve optimal performance under moderate illumination (23,000 Lux) with Macro-F1 = 0.90, representing a 15.38% improvement over other conditions. These findings reveal that reasonable matching of light source and illumination intensity can substantially enhance the accuracy of the visual recognition of coal gangue, providing valuable optimization guidance for future precise classification applications.