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卷积块注意力模块-多模特-融合动作识别:使矿工能够识别不安全的动作

Yu Wang¹, Xiaoqing Chen¹, Jiaoqun Li¹

¹School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China.

Sensors (Basel, Switzerland)

|July 27, 2024

概括

这项研究引入了一种计算机视觉模型,用于识别不安全的矿工行为,大大提高了地下采矿的安全性. 开发的模型在实时检测危险行为方面实现了高准确度.

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:

不安全的矿工行为是矿山事故的主要原因.
实时识别这些行动对于预防事故至关重要.
现有的计算机视觉方法需要改进,以便在采矿环境中进行准确的检测.

研究的目的:

开发一种先进的计算机视觉模型,用于准确和实时识别地下矿工执行的不安全操作.
通过整合注意力机制和多式联接,提高现有的动作识别模型的性能.

主要方法:

地下矿工不安全行动的构建 (UAUM) 数据集有十个行动类别.
使用空间和频域算法进行图像增强.
整合YOLOX对象检测和Lite-HRNet关键点检测,用于骨架数据提取.
开发了卷积块注意力模块-多式特征融合动作识别 (CBAM-MFFAR) 模型,结合了骨架 (CBAM-PoseC3D) 和RGB (CBAM-SlowOnly) 模式.

主要成果:

在NTU60 RGB+D数据集上,CBAM-MFFAR模型的准确度达到95.8%,在UAUM数据集上达到94.6%.
与CBAM-PoseC3D,PoseC3D,2S-AGCN和ST-GCN等现有模型相比,显著提高了准确性.
在现场试验中成功验证,准确地识别了真实采矿场的复杂和多重不安全行为.

结论:

CBAM-MFFAR模型提供了一个强大的,准确的解决方案,用于实时识别地下采矿中的不安全行为.
多式联络方法和注意力机制显著提高了识别性能.
这项技术有可能大大改善安全协议,并减少采矿行业的事故.

关键词:

行动认可行动认可注意力机制注意力机制计算机视觉计算机视觉我的智能智能地雷多式联络功能融合是多式联络的功能.

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