RSFA-Net: A High-Performance Lightweight Network for Ore Segmentation and Proportion Detection in Conveyor Belt Images

Ore segmentation and proportion detection (PD) in conveyor belt images (CBI) are significant for the safe operation of ore engineering. However, the actual accuracy of segmentation and detection is low due to the influence of ore flow shape, irregular distribution, and dust interference. This article proposed RSFA-Net, a high-performance and lightweight network for segmentation and PD in ore conveyor belts (OCB). First, a new double-layer residual depth-separable (DRDS) algorithm is proposed for the first time and applied to encoder–decoder networks. The module improves the ability of the proposed network to identify ores in images and represent complex features, while greatly reducing the parameters and computational burden required to segment the network. Second, we also propose a new channel spatial fusion attention (NCSFA) to fully tap the potential of the proposed network for ore feature extraction, which can capture complex and variable ore features and extract more critical information in the context of the image. Finally, we used high-performance image sensors to build a large, high-quality OCB dataset, solving the problem of the unavailability of public datasets. We proposed and adopted the maximum relative error (MRE) as the precision index of the ore PD task for the first time. RSFA-Net was compared to nine of the most representative ore segmentation networks. RSFA-Net outperforms all methods in terms of segmentation performance and lightweight. The segmentation accuracy of RSFA-Net is 97%, while the model size is only 1.06 MB, and the accuracy of PD is as high as 98%.