An Efficient Framework to Detect and Avoid Driver Sleepiness Based on YOLO with Haar Cascades and an Intelligent Agent

Ghizlene, Belmekki; Zoulikha, Mekkakia Maaza; Pomares, H.

doi:10.1007/978-3-030-20518-8_58

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The drivers' physical and mental states are recognized together, enhancing the unified model's inference ability. Furthermore, Belmekki Ghizlene et al [19] presented a method to quickly detect the driver's eyes to identify the driver's drowsiness by combining the Haar cascade and YOLO algorithm. Based on YOLOv4-tiny, Zuopeng Zhao et al [20] have integrated the Inception V3 architecture and RES-SEBlock module.…”

Section: A Deep Learning For Driver Behavior Detectionmentioning

confidence: 99%

CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

Liu

Wang

et al. 2022

IEEE Access

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Driver distraction behavior is prone to induce traffic accidents. Therefore, it is necessary to detect them to caution drivers in time for traffic safety. In driver behavior recognition, the variety of behaviors and the diversity of the driving environment can have a certain effect on detection accuracy, and the information loss is severe in most existing methods. These make it challenging to improve the real-time accuracy of driver distraction behavior. In this paper, we propose an improved YOLOv7 based on the channel expansion and attention mechanism for driver distraction behavior detection, named CEAM-YOLOv7. The global attention mechanism (GAM) module focuses on the key information to improve accuracy. With the insertion of GAM into the Backbone and Head of YOLOv7, the global dimensional interaction features are scaled up to enable the network to extract key features. Furthermore, In the CEAM-YOLOv7 architecture, the convolution computation has been significantly simplified, which is conducive to increasing the detection speed. Combined with the Inversion and contrast limited adaptive histogram equalization (CLAHE) image enhancement algorithm, a channel expansion (CE) algorithm for data augmentation is presented to further optimize the detection effect of infrared (IR) images. On the driver distraction IR dataset of

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Section: A Deep Learning For Driver Behavior Detectionmentioning

confidence: 99%

CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

Liu

Wang

et al. 2022

IEEE Access

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“…In this driver awareness issue there are many methods according to the parameters used to measure the sleepiness, some are based on the respiratory signal of the driver such as Yauri-Machaca et al [2] using the algorithm thoracic effort derived drowsiness index (TEDD), others focus on the mouth Akrout and Mahdi [3] by studying the spatiotemporal descriptors of a non-stationary and nonlinear signal to detect the frequency of yawning, or the eyes as much as Oliveira et al [4] makes a comparison between an (EOG) electrooculogram detection and its combination with an (ECG) electrocardiogram, or artificial vision like Amodio et al [5] uses the circular Hough transform to detect the pupil opening diameter to tell if the driver is conscious or not, when several other techniques use computer vision and a simple typed alarm to wake up the driver such as Islam et al [6]. The problematic that inspired us to start this work is that the available methods are more focusing on the detection of fatigue than its evolution or handling it; we suggest a continuity of our previous framework [7] in this interface for the detection of fatigue as well as its evolution over time and its handling with a conversational assistant to detects the state of the driver via a camera by remaining discreet enough, but who in dangerous situations marks a vigilant and moral presence to try help to take the right decisions.…”

Section: Introductionmentioning

confidence: 99%

Driver Drowsiness Detection and Tracking Based on Yolo with Haar Cascades and ERNN

Amira¹,

Zoulikha²,

Hector³

2021

IJSSE

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When it comes to dangerous drowsiness, the security of the driver and peoples surrounding him depends only on his decisions. This paper expose both of driver drowsiness detector and driving behaviour corrector method based on a conversational assistant agent able to discern and try to avoid driver sleepiness on the wheel, by using a camera to get face's images of the driver in real time, and an agent displayed in the screen and monitors the driver's face in order to warn of drowsiness and to avoid a possible accident. For that, we used Haar cascade with a simplified Yolo-Lite merged with a tree word for detection, followed by the proposed PerStat method with MLP instead of PerClos method which gave a difference of (20%). For the recognition, and helped to raise the problem of the vanishing gradient being used as sequential pre-processing for an ERNN which will generate the agent feedbacks.

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Jarvis in the car: Report on characterizing and designing in-vehicle intelligent agents workshop

Wang

Hock

Lee

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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As intelligent agents have become more popular at home, they have been progressively introduced into driving environments. Although previous research has discussed agent features and their effects on driver perception and performance, attributes that define in-vehicle agents and distinguish them from other intelligent agents have not been discussed clearly. Thus, we organized a workshop on characterizing and designing in-vehicle intelligent agents at the 13th International Conference on Automotive User Interfaces (AutoUI 2021). In this report, we integrated ideas generated during the workshop and identified user-centered action and autonomy as two attributes that define an agent, with functions and features as specific characteristics that vary agent design. The outcomes of this workshop can facilitate in-vehicle intelligent agent design and deliver optimal user experience, while providing insights on manipulating variables in controlled studies.

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An Efficient Framework to Detect and Avoid Driver Sleepiness Based on YOLO with Haar Cascades and an Intelligent Agent

Cited by 3 publications

References 13 publications

CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

Driver Drowsiness Detection and Tracking Based on Yolo with Haar Cascades and ERNN

Jarvis in the car: Report on characterizing and designing in-vehicle intelligent agents workshop

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