Application of Deep Learning in Petrographic Coal Images Segmentation

Iwaszenko, Sebastian; Róg, Leokadia

doi:10.3390/min11111265

Cited by 10 publications

(1 citation statement)

References 66 publications

(79 reference statements)

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“…With the development of deep learning technology in recent years, 22,23 deep learning-based target detection is used in the field of gangue detection, [24][25][26] boasting advantages over traditional machine learning methods in terms of no manual feature selection, excellent feature expression capability, and excellent detection accuracy. 27,28 Deep learning technology can automatically acquire and learn image features through a convolution neural network, thereby accelerating the extraction and detection of feature information in coal gangue images.…”

Section: Introductionmentioning

confidence: 99%

Coal gangue detection and recognition method based on multiscale fusion lightweight network SMS‐YOLOv3

Ren

Wang

et al. 2023

Energy Science & Engineering

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Aiming at the problems of large memory footprint, low detection speed, and low detection accuracy for small and overlapping targets existing in the current coal gangue target detection algorithm, a real-time detection method for coal gangue based on a multiscale fusion lightweight network (SMS-YOLOv3) is proposed. Taking MobileNetv3 as a feature extraction network, in which all SE modules are replaced with SKNet, thus improving the ability of image feature extraction and making more effective use of parameters.A shallow detection scale is added to form a detection structure with the fusion of four scales to improve the detection accuracy of small targets. The spatial pyramid pooling is added after the backbone network to convert different feature maps into fixed feature maps, to improve the detection accuracy of the algorithm. CIoU bounding box regression loss and the K-means++ clustering anchorbox are used to improve the detection accuracy of targets. Experimental equipment was built, and coal gangue datasets of small size, large size, dim light, mutual concealment, and a large number of coal gangue under multiple conditions were constructed. Experiment results demonstrate the effective and fast detection of the proposed algorithm for small targets and overlapping targets of coal gangue accurately, with mAP reaching 98.97%. The algorithm has an mAP improvement of 0.37% and an fps increase of 119.04% compared with the original YOLOv3, with memory only 1/24 of the original.

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Section: Introductionmentioning

confidence: 99%

Coal gangue detection and recognition method based on multiscale fusion lightweight network SMS‐YOLOv3

Ren

Wang

et al. 2023

Energy Science & Engineering

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Deep learning implementations in mining applications: a compact critical review

et al. 2023

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Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.

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