AI-Powered Vehicle Inspection System

In our solution, we leverage computer vision to automate the vehicle inspection process, ensuring accuracy, efficiency, and user satisfaction. By employing sophisticated image analysis techniques, automating image quality checks, and implementing real-time alerts, we provide a comprehensive solution to address the client's challenges.


The client faced several challenges in their vehicle inspection and damage assessment processes, which were primarily manual and prone to inaccuracies. The issues included:


  1. Manual Inspection: Users had to manually inspect five categories: left side, right side, back, front, and inside the vehicle. Incorrect picture uploads were common, complicating manual verification.
  2. Lack of Verification Tools: The absence of a checkbox for manual verification and an automatic alert mechanism to notify users about missing or incorrect images further delayed the inspection process.
  3. Manual Damage Verification: Users manually verified damages, scratches, and dents by uploading videos of the vehicle’s backside and frontside. This process involved checking operational lights, indicators, and screen damages manually without any automation, resulting in inefficiencies and potential errors.


To address these challenges, we implemented a comprehensive AI-based solution:


  1. Image Verification: We developed an AI model using computer vision algorithms that automatically verifies uploaded images against predefined inspection categories, ensuring accurate categorization. We also introduced a checkbox feature for manual verification and an automatic alert mechanism to promptly notify users of any discrepancies in image uploads.
  2. Video Analysis: We created an AI model trained on video processing algorithms to autonomously analyze uploaded videos, ensuring accurate assessment of operational lights, indicators, scratches, and damages on both the front and back sides of vehicles.

3. Segmentation: We developed a DSP app for our client to segment car parts. Initially, the app identified damages inside the car, but it was later enhanced to segment the car into sections like the door, bumper, windshield, right door, and left door. This method helped in precisely identifying where the damage occurred. The segmentation model also identified primary and secondary segments to specify the exact part of the car that was damaged.

Features & Benefits

Improved Accuracy

The solution significantly enhanced the accuracy of inspection reports by automatically detecting and correcting user errors in real-time.

Enhanced Efficiency

By automating the error detection process, the solution reduced the need for manual intervention and streamlined the overall inspection process.

Cost Savings

The reduction in manual inspection efforts and the improved accuracy of inspection data resulted in cost savings for the client.

Enhanced User Experience

Users benefited from a smoother and error-free vehicle inspection experience, leading to increased satisfaction and engagement with the inspection platform.

Technologies Used to Build the Solution

YOLO (You Only Look Once): A deep learning-based object detection algorithm used for real-time object detection tasks, Python, PyTorch, TensorFlow, AWS, FastAPI, GPU server