Size measurement of blasted rock fragments based on FRRSnet+

Guo, Qinpeng; Shi-jiao, Yang; Wang, Yuchen; Zhang, Huanbao

doi:10.1016/j.measurement.2023.113207

Cited by 1 publication

(4 citation statements)

References 24 publications

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“…In contrast, the SAM applied in this paper achieved superior performance on the two test images. The second validation test was conducted based on the data used in another study [21]. In that study, they created a deep learning-based approach to measure the size of rock fragments by combining different neural network structures.…”

Section: Performance On Another Datasetmentioning

confidence: 99%

“…It is challenging for them to deal with images with different brightness, shading, and surface texture characteristics. Therefore, it cannot be widely applied and promoted in engineering [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning-based convolutional neural networks (CNNs) have demonstrated remarkable performance across various domains of computer vision, achieving human-comparable accuracy in tasks such as image classification [22][23][24][25], object detection [26][27][28][29], and image segmentation [30][31][32]. Specifically in rock fragment segmentation, CNN-based methods have shown significant advantages over traditional image segmentation methods [21,[33][34][35][36]. Guo et al [21] proposed a network for rock fragment segmentation based on multiple CNN structures.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically in rock fragment segmentation, CNN-based methods have shown significant advantages over traditional image segmentation methods [21,[33][34][35][36]. Guo et al [21] proposed a network for rock fragment segmentation based on multiple CNN structures. A total of 14,628 labeled image patches, extracted from the entire blast rock pile images using data augmentation methods, were used for model development.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Performance On Another Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%