Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet

• Published in: PloS one 2023-09, Vol.18 (9), p.e0291115
• Main Authors: Jin, Changyu, Liang, Junyu, Fan, Chunhui, Chen, Lijun, Wang, Qiang, Lu, Yu, Wang, Kai
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Blasting; Computer and Information Sciences; Datasets; Deep learning; Engineering and Technology; Image processing; Image segmentation; Iron compounds; Management; Medical imaging equipment; Microscopy; Mineral industry; Mines; Mining; Mining engineering; Mining industry; Modelling; Molybdenum; Neural networks; Ores; Particle size; Performance evaluation; Production costs; Production management; Research and Analysis Methods; Rock masses; Rocks; Semantics; Social Sciences; Statistical analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0291115

Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.