A Brief Review and a New Automatic Method for Interpretation of Polypropylene Modified Bitumen Based on Fuzzy Radon Transform and Watershed Segmentation

Tapkın, Serkan; Zakeri, Hamzeh; Topal, Ali; Nejad, Fereidoon Moghadas; Khodaii, Ali; Şengöz, Burak

doi:10.1007/s11831-019-09323-1

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…However, the production environment on the mine is complicated; the dust interference is large, the open-air equipment has a large impact on the light, the colors and textures of the ore are different, the boundaries of the ore fragments are blurred, etc., all of which pose major challenges to any ore image segmentation technique. In response to these problems, many image processing methods have been proposed: OTSU and its improvement method [ 1 , 2 ], cluster analysis [ 3 ], watershed and its improvement methods [ 4 , 5 , 6 , 7 , 8 , 9 ], and graph-based segmentation algorithms [ 10 ]. They can segment specific ore images, but they are limited and require precise parameter adjustments.…”

Section: Introductionmentioning

confidence: 99%

An Ore Image Segmentation Method Based on RDU-Net Model

Xiao

Liu

et al. 2020

Sensors

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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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