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作 者:高勇军 郭云飞 唐文杰 GAO Yongjun;GUO Yunfei;TANG Wenjie(Guojiawan Coal Mine Branch of Yulin Shenhua Energy Co.,Ltd.,Yulin 719000,China)
机构地区:[1]国能榆林能源有限责任公司郭家湾煤矿分公司,陕西榆林719000
出 处:《煤矿机电》2025年第1期68-73,共6页Colliery Mechanical & Electrical Technology
基 金:山西省重点研发计划项目(201903D121083)。
摘 要:矿井供排水管的破裂,会对井下及时的供水和排水产生不利的影响。针对矿井供排水管破裂处人工无法快速准确定位检测的问题,提出了一种基于改进YOLOv5的矿井供排水管破裂检测方法。使用FasterNet替换YOLOv5原有的骨干网络(Backbone),以减少YOLOv5的模型体量,加快运算速度,并提高检测精度;将YOLOv5颈部(Neck)的C3模块引入MHSA(多头自注意力机制)结构,以增强水管破裂处的特征提取能力。试验结果表明,与原始的YOLOv5模型相比,改进后YOLOv5的mAP提升了4.9个百分点,达到了92.3%,Precision提升了2.3个百分点,达到了97.1%,而运行速度加快了27.52%;在模型体量方面,YOLOv5的参数量减少了18.67%,计算量减少了43.03%,模型减小了19.52%。可为实现对矿井供排水管破裂的快速准确检测提供借鉴。The rupture of mine water supply and drainage pipes will have adverse effects on timely water supply and drainage underground.A mine water supply and drainage pipe rupture detection method based on improved YOLOv5 was proposed to address the problem of manual inability to quickly and accurately locate and detect the rupture of mine water supply and drainage pipes.The original backbone network of YOLOv5 was replaced with FasterNet to reduce the model size of YOLOv5,accelerate computation speed,and improve detection accuracy.The C3 module of YOLOv5 Neck was introduced into the MHSA(Multi-head Self Attention Mechanism)structure to enhance the feature extraction capability at the site of water pipe rupture.The experimental results showed that compared with the original YOLOv5 model,the improved YOLOv5 had a 4.9 percentage point increase in mAP,reaching 92.3%,a 2.3 percentage point increase in Precision,reaching 97.1%,and a 27.52%increase in running speed.In terms of model size,YOLOv5 has reduced its parameter count by 18.67%,computational complexity by 43.03%,and model size by 19.52%.This can provide reference for the rapid and accurate detection of mine water supply and drainage pipe ruptures.
关 键 词:YOLOv5 FasterNet MHSA 目标检测 水管破裂
分 类 号:TD743[矿业工程—矿井通风与安全]
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