The technology in transportation is continuously developing due to reaching the self-driving vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It is not only limited to the conventional vehicle in which accident commonly happens, but also to the autonomous vehicle. In this paper, we proposed a detection system for recognizing pedestrians using a camera and minicomputer. The approach of pedestrian detection is applied using object detection method (YOLOv5) which is based on the Convolutional Neural Network. The model that we proposed in this paper is trained using numerous epochs to find the optimum training configuration for detecting pedestrians. The lowest value of object and bounding box loss is found when it is trained using 2000 epochs, but it needs at least 3 hours to build the model. Meanwhile, the optimum model’s configuration is trained using 1000 epochs which has the biggest object (1.49 points) and moderate bounding box (1.5 points) loss reduction compared to the other number of epochs. This proposed system is implemented using Raspberry Pi4 and a monocular camera and it is only able to detect objects for 0.9 frames for each second. As further development, an advanced computing device is needed due to reach real-time pedestrian detection.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)


    Beteiligte:


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Advanced Driver Assistance System(ADAS)

    Sangve, S M / Kulkarni, Manas / Chawade, Khushboo et al. | IEEE | 2024


    Adaptive advanced driver-assistance system (ADAS)

    DAVID BACHAR / ELAD SERFATY / ELI HAREL et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    ADAPTIVE ADVANCED DRIVER-ASSISTANCE SYSTEM (ADAS)

    BACHAR DAVID / SERFATY ELAD / HAREL ELI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Effect of Advanced Driver Assistance Systems (ADAS) on Pedestrian Safety

    Sobti, Jaskiran / Hans, Shivam / Singh, Sandeep et al. | IEEE | 2024


    Advanced Driver Assistance Systems (ADAS)

    Antony, Maria Merin / Whenish, Ruban | Springer Verlag | 2021