Image Process of Rock Size Distribution Using DexiNed-Based Neural Network

Li, Haijie; Asbjörnsson, Gauti; Lindqvist, Mats

doi:10.3390/min11070736

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…In the analysis process, the difference in light, material flow, and equipment vibration can lead to errors in the collected image pixels [32], which has a great impact on the experimental results. Therefore, it is necessary to filter and denoise the image before texture analysis [33].…”

Section: Image Filtering For Denoisingmentioning

confidence: 99%

Research on the Homogenization Evaluation of Cemented Paste Backfill in the Preparation Process Based on Image Texture Features

Yang

Jiao

et al. 2022

Minerals

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In China, cemented paste backfill (CPB) is a common treatment method after the exploitation of basic energy. The homogeneity of slurry influences the performance of CPB. However, the online monitoring and characterization of homogeneity lack relevant technologies and unified standards. This article discusses an online image analysis technique applied to the online monitoring of cemented paste backfill mixing, which is based on the evolution of the texture of images taken at the surface of the mixing bed. First, the grayscale distribution of the image obtained by the high-speed camera in the CPB preparation process was analyzed by Matlab and its variance (s2) was solved, and the texture features of the image were analyzed by the variance of grayscale distribution. Then, a homogeneity discriminant model () was established. The results show that the variance value of the grayscale distribution of the slurry image increases rapidly at first, then gradually decreases, and becomes stable in the final stage since it turns a constant value. When the s2 value tends to be stable, the slurry gradually reaches homogenization, and the discriminant coefficient of paste homogenization based on the homogenization discriminant model reaches 0.05. The homogenization prediction of CPB proves to be consistent with the backfill performance comparison results. The evolution of the texture allows obtaining important information on the evolution of different formulations during mixing, which can be used for intelligent monitoring of CPB preparation process.

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Section: Image Filtering For Denoisingmentioning

confidence: 99%

Research on the Homogenization Evaluation of Cemented Paste Backfill in the Preparation Process Based on Image Texture Features

Yang

Jiao

et al. 2022

Minerals

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“…Also, in 2021 Bai et al proposed a method based on the Watershed algorithm, the K-nearest neighbour algorithm, and the Convex shell method to segment the coal particle and determine the size distribution of coal particles (Bai et al, 2021). Furthermore, a DexiNed convolutional neural network was modified by Li et al in 2021 to determine the size distribution of aggregates on the conveyors in a crushing plant, by edge detection of rock particles images (Li et al, 2021). Moreover, Li et al developed an approach to estimate the size distribution of metallurgical cock.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

MGPB

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Fast and relatively accurate determination of the fragment size distribution of a muck-pile is still a challenge in mining operations and the existing measurement methods are inefficient. In this research, a new algorithm to determine fragment size distribution due to blasting was presented, using the image processing technique. In the newly proposed approach, delineating of the fragmented rock particles, as the main core of processing, was carried out, using a convolutional neural network. Two networks were defined and trained by 150 laboratory and 150 field data images. Also, 30 laboratory and 30 field data images were applied to carry out the validation visually, and by using F1-scores. For the two laboratory and field networks and results obtained by Split-Desktop software automatic edge detection on the same images, the F1-scores are equal to (0.98, 0.74) and (0.99, 0.85) respectively. Also, for determination of fragment size distribution by laboratory data network and Split-Desktop software automatic edge detection on the same images, the Root Mean Square Error (RMSE) for F30 and F80 are equal to (0.36, 1.20) and (0.31, 1.24) respectively. These indicate better performance of the proposed approach for both rock edge detection and fragment size distribution over Split-Desktop software automatic edge detection.

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“…The proposed model achieved robust results. Additionally, Li et al [38] used the state-of-theart edge detection model DexiNed network [39] and image morphology techniques to measure particle size on the conveyor belt in an aggregate crushing plant. The proposed method demonstrated its excellent capability to recognize rock edge features and successfully separated coarse fragments from fine contiguous areas.…”

Section: Introductionmentioning

confidence: 99%

Identification of Rock Fragments after Blasting by Using Deep Learning-Based Segment Anything Model

Zhao,

Li,

2024

Minerals

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Rock fragmentation is an important evaluation indicator for field blasting operations. This paper applies a deep learning-based method, the Segment Anything Model (SAM), to automatically segment rock fragments. To review the SAM’s segmentation performance, 83 images of rock fragment collected from the mine site were used as the test dataset. Pixel-level accuracy (PA), intersection over union (IOU), and dice coefficient (Dice) were employed to evaluate the model pixel-level segmentation performance. The results showed that the SAM exhibited excellent segmentation performance on the test data (PA = 94.5%, IOU = 94.4%, Dice = 95.4%). The coefficient of determination (R2) values for the 50% and 80% passing sizes (X50 and X80) were 0.970 and 0.991, respectively, which demonstrated that the SAM could achieve high precision measurement of rock fragmentation. Additionally, the effectiveness of the SAM was further evaluated by comparing it to commercial software, and the generalizability of the SAM was verified on two other datasets. The findings revealed that the SAM not only outperformed the Split-Desktop V 4.0 on the test dataset but also achieved comparable accuracy to previous studies on the two other datasets. The SAM could be regarded as a useful tool to provide fast and accurate feedback for field blasting.

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Image Process of Rock Size Distribution Using DexiNed-Based Neural Network

Cited by 13 publications

References 14 publications

Research on the Homogenization Evaluation of Cemented Paste Backfill in the Preparation Process Based on Image Texture Features

Research on the Homogenization Evaluation of Cemented Paste Backfill in the Preparation Process Based on Image Texture Features

Untitled

Identification of Rock Fragments after Blasting by Using Deep Learning-Based Segment Anything Model

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