Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies
Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of int...
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| description | Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare. |
| doi_str_mv | 10.1371/journal.pone.0304669 |
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This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0304669</identifier><identifier>PMID: 38985745</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adaptability ; Adult ; Advanced driver assistance systems ; Alertness ; Algorithms ; Analysis ; Artificial neural networks ; Automobile Driving ; Behavior ; Biology and Life Sciences ; Biometry ; Brightness ; Computer and Information Sciences ; Datasets ; Diagnosis ; Dimensional analysis ; Driver fatigue ; Efficiency ; Electric lighting ; Engineering and Technology ; Environmental conditions ; Equipment and supplies ; Face recognition ; Facial Recognition - physiology ; Facial recognition technology ; Fatigue ; Female ; Health aspects ; Humans ; Identification ; Lighting ; Lighting - methods ; Male ; Medicine and Health Sciences ; Network reliability ; Neural networks ; Neural Networks, Computer ; Pattern recognition ; Physical Sciences ; Physiological aspects ; Research and Analysis Methods ; Roads & highways ; Social Sciences ; System effectiveness ; System reliability ; Technology application ; Technology assessment ; Traffic accidents & safety ; Traffic safety ; Vehicles</subject><ispartof>PloS one, 2024-07, Vol.19 (7), p.e0304669</ispartof><rights>Copyright: © 2024 Lin, Zuo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Lin, Zuo. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Lin, Zuo 2024 Lin, Zuo</rights><rights>2024 Lin, Zuo. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.</description><subject>Accuracy</subject><subject>Adaptability</subject><subject>Adult</subject><subject>Advanced driver assistance systems</subject><subject>Alertness</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Automobile Driving</subject><subject>Behavior</subject><subject>Biology and Life Sciences</subject><subject>Biometry</subject><subject>Brightness</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Dimensional analysis</subject><subject>Driver fatigue</subject><subject>Efficiency</subject><subject>Electric lighting</subject><subject>Engineering and Technology</subject><subject>Environmental 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Ning</au><au>Zuo, Yue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-07-10</date><risdate>2024</risdate><volume>19</volume><issue>7</issue><spage>e0304669</spage><pages>e0304669-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38985745</pmid><doi>10.1371/journal.pone.0304669</doi><tpages>e0304669</tpages><orcidid>https://orcid.org/0009-0008-7407-3014</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Adaptability Adult Advanced driver assistance systems Alertness Algorithms Analysis Artificial neural networks Automobile Driving Behavior Biology and Life Sciences Biometry Brightness Computer and Information Sciences Datasets Diagnosis Dimensional analysis Driver fatigue Efficiency Electric lighting Engineering and Technology Environmental conditions Equipment and supplies Face recognition Facial Recognition - physiology Facial recognition technology Fatigue Female Health aspects Humans Identification Lighting Lighting - methods Male Medicine and Health Sciences Network reliability Neural networks Neural Networks, Computer Pattern recognition Physical Sciences Physiological aspects Research and Analysis Methods Roads & highways Social Sciences System effectiveness System reliability Technology application Technology assessment Traffic accidents & safety Traffic safety Vehicles |
| title | Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies |
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