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CGALS-YOLO:基于视觉的传感用于在地下环境中检测防护设备的服装合规性

Chao Huang¹, Hongkang Huang¹

¹School of Computer Science and Technology, Zhejiang University of Science and Technology, 318 Liuhe Road, Liuxia Subdistrict, Xihu District, Hangzhou 310023, China.

Sensors (Basel, Switzerland)

|March 14, 2026

概括

本研究介绍了CGALS-YOLO,这是一个基于视觉的系统,用于检测矿山中的防护设备合规性. 它在具有挑战性的地下条件下提高了检测精度,提高了工人安全.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
采矿安全工程采矿安全工程

背景情况:

地下采矿安全依赖于防护设备,但基于视觉的监控面临着诸如照明不良和尺度变化等挑战.
现有的物体检测模型在复杂的采矿场景中难以处理冗余的背景信息和小目标.

研究的目的:

开发一种有效的基于视觉的方法,用于检测地下采矿中的防护设备是否符合规定.
提高安全监测系统在具有挑战性的地下环境中的准确性和可靠性.

主要方法:

拟议的CGALS-YOLO模型集成了一个内容引导的特征融合 (CGAFusion) 模块和一个轻量级的共享卷积检测 (LSCD) 结构.
CGAFusion使用注意力机制来适应特征融合,增强目标感知和减少背景噪音.
LSCD优化了检测头,以减少参数和提高跨尺度检测的一致性.

主要成果:

CGALS-YOLO实现了89.4%的mAP@0.5,比起基线YOLOv8n.1,提高了4.6%的检测准确度和3.1%的召回.
该模型显示了23.9%的参数减少和较低的计算复杂性.
有效地抑制背景干扰,在复杂的地下场景中增强小规模目标检测.

结论:

拟议的CGALS-YOLO方法显著增强了基于视觉的安全监测,以确保地下采矿的保护设备符合要求.
新的CGAFusion和LSCD模块为改善在具有挑战性的环境中检测性能提供了实际解决方案.
在地下采矿中实时安全监控的验证有效性和实际适用性.

关键词:

在CGAFusion中使用.在 LSCD 中,我们可以看到 LSCD.地下地下地铁是什么意思穿着合规检测检测器检测合规性

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