Vehicle safety is the primary and necessary aspect of the automobile industry. An airbag is one of the passive safety systems available in an automobile. However, the airbag deployment needs to be controlled to avoid accidents due to it. In low-velocity crashes, the injury caused by airbag deployment is higher than the impact inside the vehicle. Children are more vulnerable to airbags when they sit near airbag housing without proper seatbelts or child seat arrangements. Deployment of the airbag when no occupant is sitting on a seat is unnecessary. So, it is important to detect the occupants’ presence in a seat and their classes such as a child or an adult. The primary aim of the paper is to detect the occupants’ presence and classify them into different classes. The occupant classes used are child and adult. In this project, we developed a technique to identify the occupants’ presence and verify the data from one sensor using another. We collected the image data of the occupants using a camera in a sedan and hatchback vehicles. We analyzed the images using a deep learning algorithm. The output classified the occupants as child and adult. A load cell sensor mounted on the seat was used to measure the weight of the passenger. This data was used to confirm the occupant classification. We evaluated the model detection and classification performances with the parameters such as precision 0.95, recall 0.97, and F1-score 0.96 for image dataset, and we got 0.73 as a classification accuracy for load cell dataset. Finally, we compared both the model performances.


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    Title :

    Artificial Intelligence-based Vehicle In-Cabin Occupant Detection and Classification System


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:


    Publication date :

    2022-07-06


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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