An Ore Image Segmentation Method Based on RDU-Net Model

Abstract

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

“…Shubham et al [15] constructed a convolutional neural network based on Mask R-CNN [16] for predicting the size of rock fragments. Xiao et al [17] combined the residual structure of convolutional neural networks with the DUNet model [18] to propose a rock image segmentation model called RDU-Net. This model can dynamically adjust the receptive field based on the size and shape of different rock fragments.…”

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

“…Multiple authors have studied conveyor belt foreign body detection methods based on deep learning over the past several years.Together these studies provide important insights into precision and speed of foreign body detection. Xiao et al [10] to detect coal gangue, a human measurement model was proposed that combines the RDU-Net model with the convolutional neural network's residual structure. This model significantly increases the accuracy of image recognition.…”

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

“…Li et al [ 15 ] proposed a method for detecting coal and gangue in images that uses the YOLOv3 algorithm to identify coal gangues. Xiao et al [ 16 ] suggested using a more objective approach to image segmentation based on the RDU net model, which combines the residual structure of the convolutional neural network with the dunet model. Zhang et al [ 17 ] presented a method for detecting foreign objects in coal using machine vision based on an attention neural network.…”