VISION ALGORITHM DEVELOPMENT ARTIFICIAL TO DETECT THE DROWSINESS IN DRIVERS HEAVY MACHINERY MINERS
DOI:
https://doi.org/10.18687/LACCEI2023.1.1.1048Palabras clave:
Drowsiness, Python, computer vision, landmarks, threshold.Resumen
The main objective of this research is to develop an artificial vision algorithm that detects the drowsiness of heavy machinery mining drivers, using an artificial vision architecture with Python software, importing face detection libraries such as shape_predictor_68_face_landmarks with which it will be detected and They will identify each important point. In the input stage, the face will be detected with a camera placed in a strategic point of the vehicle and/or machine. Then the software detects and measures the required points. A threshold 0.22 was used at a time of 60 fps (1sec) to determine if the individual is blinking continuously or due to fatigue, if this is the case an alert message will be sent. In the tests carried out we obtained average positive results of 96.48%.Descargas
Publicado
2023-07-27
Número
Sección
Articles
Derechos de autor
Derechos de autor 2023 LACCEI
Licencia
Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).
Cómo citar
Figueroa Herrera, Brenda Mariana, Sánchez Burgos, Flavio Cesar, & León León, Ryan Abraham. (2023). VISION ALGORITHM DEVELOPMENT ARTIFICIAL TO DETECT THE DROWSINESS IN DRIVERS HEAVY MACHINERY MINERS. LACCEI, 1(8). https://doi.org/10.18687/LACCEI2023.1.1.1048