Dynamic set point model for driver alert state using digital image processing

Isaza, César; Anaya, Karina; Fuentes-Silva, Carlos; Paz, Jonny Paul Zavala de; Rizzo, Amilcar; García-Moreno, Angel-Iván

doi:10.1007/s11042-019-7218-z

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…For instance, Mollanosscini et al 17 proposed a modified GoogleNet model-based single component network architecture that achieved improved results in facial expression recognition. Cesar Isaza introduced a dynamic setpoint model for detecting face parts and determining fatigue status 18 . Huynh et al designed a 3D CNN with a gradient enhancement algorithm to extract key features from video images for human fatigue state determination; however, the DDD dataset poses considerable challenges and requires substantial computational resources 19 .…”

Section: Study Of Facial Features Based On Deep Learning Methodsmentioning

confidence: 99%

Intelligent Bell facial paralysis assessment: a facial recognition model using improved SSD network

Shi,

Fan,

Zhang

et al. 2024

Sci Rep

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With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients’ facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell’s facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients’ condition. By replacing the VGG network with a more efficient backbone, we improved the model’s accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation.

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Section: Study Of Facial Features Based On Deep Learning Methodsmentioning

confidence: 99%

Intelligent Bell facial paralysis assessment: a facial recognition model using improved SSD network

Shi,

Fan,

Zhang

et al. 2024

Sci Rep

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“…Survey of machine learning-based different IoT application in traffic engineering, security with challenges and open issues is explained in [5,9]. Face and fatigue detection using Haar descriptors with the help of Raspberry Pi board, USB camera, Open CV libraries, and Python is explained in [10]. Hybrid smart parking module, with a survey of the use of different sensors under smart city development using IoT, is explained in [2].…”

Section: Literature Surveymentioning

confidence: 99%

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Trivedi¹,

Devi²,

Dhara³

2020

SJSUT.ST

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Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking "Available" or "Not available" for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart.

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“…To sum up, most current studies mainly detect fatigue by extracting overall behavior features and partial face features [6,7]. However, such a way of overall behavior or partial characteristics may have some problems, such as the loss of some key characteristics and the single fatigue index that can be referred to, so it is difficult to show the fatigue state.…”

Section: Introductionmentioning

confidence: 99%

Driver fatigue detection based on lightweight MobileNetV2

Wang

2023

ACE

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Fatigue driving brings a huge potential threat to the property and life safety of drivers and pedestrians. In order to improve the intelligence level of driver fatigue driving detection, a driver fatigue detection method based on MobileNetV2 lightweight separable convolutional neural network is proposed in this study. Firstly, the driver images in the data are pre-processed to make the data compatible with the model and improve accuracy, then the MobileNetV2 network model is used to detect and extract key features (mainly eye and mouth information) from the face, and finally the softmax function is used to classify the acquired features (based on the trained parameters, which have been obtained to discriminate fatigue) and determine whether the driver is in a fatigue state. The experimental results show that the proposed method has the satisfactory performance in terms of the accuracy, can basically adapt to driving scenes under different lighting, and has high robustness.

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Dynamic set point model for driver alert state using digital image processing

Cited by 9 publications

References 20 publications

Intelligent Bell facial paralysis assessment: a facial recognition model using improved SSD network

Intelligent Bell facial paralysis assessment: a facial recognition model using improved SSD network

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Driver fatigue detection based on lightweight MobileNetV2

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