Road defects such as potholes, cracks, and other flaws can harm automobiles, impair a driver's ability to drive safely, and in some circumstances cause serious traffic accidents. Therefore, road conditioning methods need to be implemented for developing and maintaining roads. Prior until now, detection of potholes was done only by visual inspection by human experts which is a time-consuming process and can even be inaccurate at times. Country's economy depends on well-maintained roads as they are major means of transportation. It becomes essential to identify pothole and humps in order to avoid accidents and damages to the vehicles that is caused because of distress to drivers and also to save fuel consumption. In this regard, this prototype work presents a simple solution to detect potholes and humps and hence maintaining roads in good quality and avoiding accidents. Potholes are detected using Image Processing Technique with OpenCV and Python are used to detect road defects and rectify them by the means of a dispensing mechanism. A Raspberry Pi 4 Model B microprocessor is used on-board the AVPD for controlling it. This project presents an attempt to design and constructing a prototype of an automated road repair vehicle called the Autonomous vehicle for Pothole Detection (AVPD). The AVPD is capable of automatically detecting and filling potholes on road surfaces without operator assistance. An easy-to-construct mechanical means of pothole detection was employed to reduce costs and complexity that have thus far been the primary disadvantage of automated road repair vehicles. A network interface using Wi-Fi was designed based on IOT to enable remote operability of the AVPD. The vehicle first detects the pothole, dispenses the pothole filler material, flattens the filled material andthen goes ahead to detect another pothole.


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    Titel :

    Intelligent Vehicle for Simulated Pothole Detection Using Image Processing


    Beteiligte:
    Garje, Pranav (Autor:in) / Patle, B.K. (Autor:in)


    Erscheinungsdatum :

    01.11.2023


    Format / Umfang :

    1003511 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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