Seat Belt Detection Using Convolutional Neural Network BN-AlexNet

Zhou, Bin; Chen, Dongfang; Wang, Xiaofeng

doi:10.1007/978-3-319-63309-1_36

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Research also attempted to improve accuracy and speed by developing CNN models. Zhou et al developed the Alexnet model by adding Batch Normalization (BN), producing BN-Alexnet [Zhou et al, (2017)]. The test results showed that this proposed method increased the average correct detection and reduced the training time compared to Alexnet, VGGNet-16, and GoogLeNet.…”

Section: Non-handcrafted Featurementioning

confidence: 99%

Untitled

2023

INDJCSE

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Traffic accident fatalities are still a severe problem in the world, and one of the contributing factors remains the failure of drivers to adhere to essential traffic regulations, exemplified by their noncompliance with seat belt usage. Previous research has developed systems for detecting drivers not wearing seat belts, consisting of three subsystems: car detection, windshield detection, and seat belt detection. Several reviews for car detection have been done, but a review focusing on seat belt detection has never been carried out. Therefore, this paper aims to review seat belt detection. The initial section outlines general steps to detect drivers not wearing a seat belt. The following section compares the methods used for seat belt detection. The research concludes by describing potential methods to improve accuracy and speed.

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Section: Non-handcrafted Featurementioning

confidence: 99%

Untitled

2023

INDJCSE

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“…Guo et al [11] similarly utilized an edge detection algorithm to detect seat belt from traffic surveillance cameras. Zhou et al [38] used AlexNet [17] with batch normalization [15] to identify seat belts. Elihos et al [9] proposed a method that crops passenger regions first using the single shot detector (SSD) [20] and applies a CNN to detect seat belt non-use.…”

Section: Seat Beltmentioning

confidence: 99%

NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks

Chun

Ghalehjegh

Choi

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble and thus more suitable for in-vehicle monitoring tasks compared to other generic pose estimation algorithms. The new architecture, named NADS-Net, utilizes the feature pyramid network (FPN) backbone with multiple detection heads to achieve the optimal performance for driver/passenger state detection tasks. The new architecture is validated on a new data set containing video clips of 100 drivers in 50 driving sessions that are collected for this study. The detection performance is analyzed under different demographic, appearance, and illumination conditions. The results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development and data collection.

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“…Additionally, Elihos et al used Fisher Vector [3]. Most research used non-handcrafted features, such as BN-AlexNet [4], Convolutional Neural Network (CNN) [5], A Nimble Architecture for Driver and Seat Belt Detection through Convolutional Neural Networks (NADS-Net) [6], YOLOv3 [7], and YOLOv5 [8], [9]. YOLOv5 achieved the best mean Average Precision (mAP) compared to other methods for detecting vehicle windshields, passengers, and drivers without seat belts [9].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

Sugiharto,

Kusumaningrum

2023

IJACSA

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The issue of traffic accident fatalities is a serious concern on a global scale, and one of the contributing factors is the failure of drivers to adhere to seat belt usage. A notable challenge arises from the limited availability of law enforcement personnel monitoring this particular issue. In this context, there is a compelling need to implement an automated detection system. The development of this system using YOLOv5 has been done. However, there are weaknesses related to the length of training and detection time. Therefore, this paper proposed a new system using the YOLOv8 method to detect drivers and passengers who violate seat belt regulations. The proposed system is divided into three subsystems: windshield detection, passenger classification, and seat belt classification. YOLOv8 is the latest version of the YOLO (You Only Look Once) method and has been proven to provide better performance than previous versions. Furthermore, this paper also compared five YOLOv8 models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. The proposed model is trained and tested using image data collected from several roads in Indonesia. The experiment results show that the YOLOv8s model produced the best mean Average Precision (mAP) of 0.960 for windshield detection. YOLOv8s-cls and YOLOv8l-cls models achieved the same accuracy of 0.8923 for passenger classification. The YOLOv8l-cls model produced the best accuracy of 0.8846 for seat belt classification. In addition, the proposed method can increase mAP and training time for windshield detection compared to YOLOv5.

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Seat Belt Detection Using Convolutional Neural Network BN-AlexNet

Cited by 9 publications

References 9 publications

Untitled

Untitled

NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

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