Borehole Depth Recognition Based on Improved YOLOX Detection

This study proposes a method for recognizing the drill depth in low-light underground environments, with the aim of addressing the issues of low efficiency and susceptibility to manual changes in the current methods. The method is based on an improved You Only Look Once X model. Initially, image dat...

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Bibliographic Details
Published inComputer journal Vol. 67; no. 7; pp. 2408 - 2420
Main Authors Ren, Dawei, Meng, Lingwei, Wang, Rui
Format Journal Article
LanguageEnglish
Published Oxford University Press 20.07.2024
Subjects
Attention mechanism
Loss function
Recognizing the drill depth
Data inhancement
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Summary:This study proposes a method for recognizing the drill depth in low-light underground environments, with the aim of addressing the issues of low efficiency and susceptibility to manual changes in the current methods. The method is based on an improved You Only Look Once X model. Initially, image data undergo enhancement and annotation. Secondly, it incorporates an attention mechanism to improve the feature extraction capability. The feature pyramid is utilized to minimize feature loss and facilitate better multi-scale feature fusion. Additionally, the loss function is optimized to enhance the localization ability of the prediction box. The enhanced model achieves an accuracy of 91.3$\%$, representing a 4.4$\%$ increase compared to the pre-improvement performance, and demonstrates improved positioning accuracy. Successful drilling depth measurements were carried out with the acquired positioning information.
ISSN:0010-4620
1460-2067
DOI:10.1093/comjnl/bxae015