Efficient Image Segmentation of Coal Blocks Using an Improved DIRU-Net Model

Coal block image segmentation is of great significance for obtaining the particle size distribution and specific gravity information of ores. However, the existing methods are limited by harsh environments, such as dust, complex shapes, and the uneven distribution of light, color and texture. To address these challenges, based on the backbone of the U-Net encoder and decoder, and combining the characteristics of dilated convolution and inverted residual structures, we propose a lightweight deep convolutional network (DIRU-Net) for coal block image segmentation. We have also constructed a high-quality dataset of conveyor belt coal block images, solving the problem that there are currently no publicly available datasets. We comprehensively evaluated DIRU-Net in the coal block dataset and compared it with other state-of-the-art coal block segmentation methods. DIRU-Net outperforms all methods in terms of segmentation performance and lightweight. Among them, the segmentation accuracy rate reaches 94.8%, and the parameter size is only 0.77 MB.