An Ore Image Segmentation Method Based on RDU-Net model

Sensors

BaTuan Le¹  Zhiwen Ji¹  Dong Xiao¹  Xiwen Liu¹  Xiaoyu Sun² 

[1] Information Science and Engineering School, Northeastern University, Shenyang 110004, China;School of Resources and Civil Engineering, Northeastern University, Shenyang Liaoning 110000, China;

关键词: ore image;    conveyor belt;    image segmentation;    DUNet;    residual connection;   

DOI  :  10.3390/s20174979

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown