煤矿安全2025，Vol.56Issue(4)：166-173,8.DOI:10.13347/j.cnki.mkaq.20240477
煤矿井下顶板突水征兆视频智能识别方法研究
Research on intelligent video recognition method for roof water inrush signs in coal mines
摘要
Abstract
Water inrush incidents constitute a component of coal mine disasters,and the accurate identification of roof water inrush signs is crucial for preventing accidents.However,the underground environment of coal mine is complex and changeable,and the signs of water inrush are mainly found by manual judgment at present.Due to the influence of harsh underground environment and subjective factors,it is difficult to find anomalies in time,which makes it difficult to effectively identify the signs of water inrush in the roof.In order to efficiently monitor and accurately identify water inrush signs,a self-supervised water inrush sign recognition method based on SAM-XMem is proposed based on image recognition and processing technology.This method employs pixel change rates to construct the water inrush warning system.It autonomously annotates images collected from water inrush-prone areas within coal mines and segments water inrush sign regions using the SAM model.By integrating the XMem long video segmentation framework,real-time dynamic tracking of these regions is achieved.The results demonstrate that compared with the OTSU al-gorithm,the self-supervised recognition algorithm based on SAM-XMem exhibits superior performance in terms of intersection over union(IoU),precision(P),recall(R),and F1 score,with evaluation metrics exceeding 90%,which represents 20%improvement over the recognition rate of conventional algorithms.The problem of data set shortage in coal mine is overcome by zero sample segmenta-tion technology.Compared with the traditional method,the feature extraction ability is stronger,and the effect is better under com-plex conditions such as large background noise and small gray value difference.It is suitable for underground coal mine environ-ment,and has higher recognition accuracy and stronger generalization ability.关键词
矿井水害/顶板突水/突水征兆/SAM模型/图像分割技术Key words
mine water hazard/roof water inrush/water inrush sign/SAM model/image segmentation technology分类
矿业与冶金引用本文复制引用
连会青,康佳,尹尚先,徐斌,闫国成,夏向学,徐保同..煤矿井下顶板突水征兆视频智能识别方法研究[J].煤矿安全,2025,56(4):166-173,8.基金项目
国家重点研发计划资助项目(2022YFC3005904-1) （2022YFC3005904-1）
国家自然科学基金资助项目(51774136,51974126) （51774136,51974126）
中央引导地方科技发展资金资助项目(246ZT604G) （246ZT604G）
