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.
Highlights
In the mining industry, the early production of minerals includes three steps: blasting, crushing and grinding
Based on DUNet, in order to solve the problem of information loss between convolutions and protect the integrity of information, this paper introduces the residual structure of ResNet [29], and proposes a novel ore image segmentation model called RDU-net, which has better segmentation effect
The computer used for image segmentation model training in this article is configured with an Intel Core i5-7500 3.40 GHz processor, NVIDIA GTX 1050 Ti graphics card, 16 GB RAM and a
Summary
The early production of minerals includes three steps: blasting, crushing and grinding. Ibtehaz et al [25] replaced the sequence of two convolutional layers with MultiRes blocks and developed a novel architecture MultiResUNet. Liu et al [26] proposed an improved osteoporosis diagnosis algorithm based on U-NET network. Based on DUNet, in order to solve the problem of information loss between convolutions and protect the integrity of information, this paper introduces the residual structure of ResNet [29], and proposes a novel ore image segmentation model called RDU-net, which has better segmentation effect.
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