Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks.
Background: During driving, drowsiness may happen for a few moments, but its consequences can be terrible. Drowsiness in the driver can be detected in the early stages. Each method used for detecting drowsiness has its own strengths and weaknesses or benefits and flaws. The main contribution of our...
| Publicado en: | Iranian Journal of Public Health Vol. 54; no. 9; pp. 2024 - 2035 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Tehran University of Medical Sciences
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188509620&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188509620 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22516085 6N6F jtl: Iranian Journal of Public Health issn: 22516085 maglogo: N pubinfo: dt: Sep2025 vid: 54 iid: 9 pid: 21783 pub: Tehran University of Medical Sciences artinfo: ui: 188509620 188509620 188509620 188509620 ppf: 2024 ppct: 11 formats: tig: atl: Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks. aug: au: Askari, Ali Sahlabadi, Ali Salehi Eshaghzadeh, Maliheh Poursadeghiyan, Mohsen Saraji, Gebraeil Nasl affil: Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Sleepiness Diagnosis Accidents, Traffic Prevention and Control Diagnosis, Computer Assisted Image Processing, Computer Assisted Neural Networks (Computer) Software Design Evaluation Reliability Evaluation Automobile Driving Human Female Male Sleep Deprivation Sleep Stages Models, Theoretical Software Facial Expression Machine Learning Algorithms Sensitivity and Specificity Students, College Descriptive Statistics Experimental Studies Scales Funding Source Occupational Health Sleepiness Physiopathology Signal Processing, Computer Assisted Female Male ab: Background: During driving, drowsiness may happen for a few moments, but its consequences can be terrible. Drowsiness in the driver can be detected in the early stages. Each method used for detecting drowsiness has its own strengths and weaknesses or benefits and flaws. The main contribution of our research was improving Driver Drowsiness Detection (D.D.D) systems. Methods: In accordance with the research objective, it is imperative to address the subsequent inquiries (Q) throughout the process of constructing, testing, and delivering the ultimate D.D.D software model: Q1. What is the methodology employed for constructing the initial model of drowsiness detection software? Q2. How is the initial model of drowsiness detection software tested and refined during the development phase? Q3. What is the operational mechanism of the final model of drowsiness detection software? Results: The results were able to detect different facial conditions (with hair and glasses) with a 92.3 percentage detection rate. Conclusion: This model could help improve D.D.D systems, and detect drowsiness in different environments and situations. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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