Forklifts, commonly used in modern factories for transporting goods, are often driven with agility, which can lead to safety hazards. In this study, we present a real-time, on-board monitoring system based on edge computing and deep learning to assist forklift operators for more safe driving. Serving as the forklift's peripheral vision, a wide-angle imaging module with three cameras is designed to detect pedestrians and designated object (e.g. traffic cone, oil drum, truck, bicycle, etc.) around the vehicle. This system actively identifies pedestrians and objects around the forklift's path to warn the operator and enhance safety. Additionally, a wide-angle camera is also installed in front of the operator to monitor the driver’s driving behavior. The system processes images at a speed of over 20 frames per second, making it suitable for real-time pedestrian detection and collision avoidance.


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    Title :

    Forklift Collision Avoidance System Based on AI and Edge Computing


    Additional title:

    Communic.Comp.Inf.Science


    Contributors:

    Conference:

    International Symposium on Pervasive Systems, Algorithms and Networks ; 2025 ; Bangkok, Thailand January 19, 2025 - January 23, 2025
    International Conference on Ubi-Media Computing ; 2025 ; Bangkok, Thailand
    January 19, 2025 - January 23, 2025



    Publication date :

    2025-07-20


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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