In the automobile manufacturing industry, quality and safety testing of vehicles prior to delivery is a significant process. Traditionally, such acceptance forms such as vehicle history cards are inspected manually to confirm that there are significant seals and stamps that assure a vehicle has undergone all the required quality tests. This is a time- consuming process that relies on human error. To address these limitations, this paper proposes an automated system using OpenCV for the verification of vehicle quality inspection reports. The system reads images of vehicle history cards and processes them to detect mandatory stamps and seals that attest to approval. Through computer vision techniques such as thresholding, contour detection, and template matching, the system detects and verifies the required markings based on predefined standards. This automation results in lower dependence on human labor, significantly boosts processing speed, and better overall verification accuracy. Experimental evaluation confirms that the suggested approach is able to effectively minimize errors and streamline the approval process. The proposed solution can be scaled up and easily integrated with any documentation format, making it a valuable resource for automotive manufacturing lines. Ultimately, the system facilitates a more effective and reliable quality control process that only sends safe and quality-approved vehicles to delivery.


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    Titel :

    Vehicle Confirmation Passing Detection Using Data Augmentation and Gradio


    Beteiligte:


    Erscheinungsdatum :

    28.05.2025


    Format / Umfang :

    641384 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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