王伟, 李擎, 张德政, 栗辉, 王昊. 基于深度学习的矿石图像处理研究综述[J]. 工程科学学报, 2023, 45(4): 621-631. DOI: 10.13374/j.issn2095-9389.2022.01.23.001
引用本文: 王伟, 李擎, 张德政, 栗辉, 王昊. 基于深度学习的矿石图像处理研究综述[J]. 工程科学学报, 2023, 45(4): 621-631. DOI: 10.13374/j.issn2095-9389.2022.01.23.001
WANG Wei, LI Qing, ZHANG De-zheng, LI Hui, WANG Hao. A survey of ore image processing based on deep learning[J]. Chinese Journal of Engineering, 2023, 45(4): 621-631. DOI: 10.13374/j.issn2095-9389.2022.01.23.001
Citation: WANG Wei, LI Qing, ZHANG De-zheng, LI Hui, WANG Hao. A survey of ore image processing based on deep learning[J]. Chinese Journal of Engineering, 2023, 45(4): 621-631. DOI: 10.13374/j.issn2095-9389.2022.01.23.001

基于深度学习的矿石图像处理研究综述

A survey of ore image processing based on deep learning

  • 摘要: 聚焦于矿石勘探和将矿石破碎筛分后的皮带运输两个环节,系统总结了深度学习技术在矿石图像处理中的主要应用,包括矿石分类、粒度分析和异物识别等任务,并分门别类地梳理了完成以上三大任务的常用算法及其优缺点。其中,矿石分类在地质勘探中起着重要作用;粒度分析能为破碎机和传送皮带的控制提供参考依据,还能识别出给矿皮带上过大尺寸的矿石,防止处于给矿皮带和受矿皮带之间的转运缓冲仓内发生堵料事故;异物识别能将皮带上混在矿石中的有害物品检测出来。

     

    Abstract: Ore is an essential industrial raw material and strategic resource that plays an important role in China’s economic construction. The smart mine aims to build an unmanned, efficient, intelligent, and remote factory to improve quality, reduce cost, save energy, and increase the efficiency of mineral resource extraction. Ore image processing technology can automatically and efficiently complete a series of difficult and repetitive tasks, which constitutes an important part of smart mine construction. However, open-air operation modes, high-dust environments, and ore diversity have brought great challenges to ore image processing. Benefiting from its strong automatic feature extraction ability, deep learning can deeply perceive a complex environment, which enables it to play an important role in the ore image processing field and help traditional mining companies transform into efficient, green, and intelligent enterprises. This paper focuses on two production stages, including ore prospecting and belt transportation. We systematically summarize the main applications of deep learning in ore image processing, including ore classification, particle size analysis, and foreign material recognition, sort out the corresponding algorithms, and analyze their advantages and disadvantages. Specifically, according to the number of ores in an image, ore classification is divided into single-object and multi-object classifications. Single-object classification is mostly addressed by image classification networks, while multi-object classification is mostly accomplished by object detection and semantic segmentation networks. Single-object classification plays an important role in geological prospecting. Particle size refers to the size information of ores in an image. Generally, it can be divided into three modes: particle size statistics, particle size classification, and large block detection. Among these modes, the first and the third are mainly used in actual industrial production. Particle size statistics are determined mostly using semantic segmentation networks and can provide a reference for the control of crushers and conveyor belts. Large block detection is performed mostly by adopting object detection networks and can identify the oversized ore on an ore feeding belt and prevent material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Foreign material recognition detects harmful objects mixed in the ores on the belt to ensure product quality and prevent the belt from tearing. Object detection technology is often used to complete the task of foreign material recognition.

     

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