In Automotive Industry, instrument clusters are used in all type of vehicles. It is an electronic instrument which displays and shows system function status, warnings, and failures/alerts. These displays and tell-tales help driver to get necessary information to drive the vehicle. Several gauges, such as speedometer, odometer, fuel-gauge etc, and other tell-tales for system failures and alerts, are included in vehicle dashboard. Instrument clusters give drivers a concentrated and easily accessible area for seeing all vital system data. The need of validation for instrument clusters become extremely important. The defects shall be caught at early stage to improve system behaviour and save product development cost. So, this is highly demanding to minimise manual testing time and effort while increasing testing accuracy to deliver defect-free product to customers. We are proposing test automation using camera to verify test results of instrument cluster using machine learning algorithm. The scope of this paper is to design a deep learning model that will be used to train, test and validate the model in HIL bench. The data base for which will be made using data scrapping from open-source images and image collected from existing IC The model will be integrated into an existing HIL bench automation setup. Using several python scripts for data collection and Yolov5 machine learning algorithm for object detection the accuracy is increased by 25% and time reduced by 24.35%.


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    Titel :

    Machine Learning Based Instrument Cluster Inspection Using Camera


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Bose, Indranil (Autor:in) / Bobade, Saloni (Autor:in) / Anilkumar, Sandhya (Autor:in) / Tavhare, Sarika (Autor:in) / Kalkar, Shreyas (Autor:in)

    Kongress:

    10TH SAE India International Mobility Conference ; 2022



    Erscheinungsdatum :

    2022-10-05




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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