Affiliation:
1. College of Energy and Mining Engineering, Shandong University of Science and Technology , Qingdao 266590 , China
Abstract
Abstract
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.
Funder
Humanities and Social Sciences Youth Foundation
Ministry of Education
Natural Science Foundation of Shandong Province
Qingdao Postdoctoral Funding Project
Publisher
Oxford University Press (OUP)
Reference32 articles.
1. State of the art: investigation on mechanism, forecast and control of coal bumps in China;Jiang;Chin. J. Rock Mech. Eng.,2015
2. Detection algorithm of safety helmet wearing based on deep learning;Huang;Concur. Comput.: Pract. Exp.,2021
3. Human movement recognition based on the stochastic characterisation of acceleration data;Munoz-Organero;Sensors,2016
4. An efficient fine-grained vehicle recognition method based on part-level feature optimization;Lei;Neurocomputing,2023
5. Lung cancer cell recognition based on multiple color space;Lu
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