ArticleSensors (Basel, Switzerland)2020
An Ore Image Segmentation Method Based on RDU-Net Model.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- VTC-Net: A Semantic Segmentation Network for Ore Particles Integrating Transformer and Convolutional Block Attention Module (CBAM).Sensors (Basel, Switzerland) · 2026Article
- Hazard source detection of longitudinal tearing of conveyor belt based on deep learning.PloS one · 2023Article
- Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet.PloS one · 2023Article
- An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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