Ai damage detector

Inspiration

We were inspired by how vehicle damage inspection is still done manually, which takes time and depends on human judgment. Insurance claims and service checks can be slow and sometimes inconsistent. We wanted to use AI to make this process faster, smarter, and more reliable.

What it does

AI Damage Detector analyzes vehicle images and detects whether there is damage. It can identify types of damage like dents or scratches and gives a confidence score. This helps in quick inspection and decision-making.

How we built it

We built the project using deep learning and computer vision. We collected and labeled vehicle images, preprocessed them, and trained a Convolutional Neural Network (CNN) model. We improved performance using data augmentation and transfer learning. Finally, we created a simple web interface where users can upload images and get instant results.

Challenges we ran into

Finding a good dataset was difficult. Different lighting conditions and camera angles affected accuracy. We also faced overfitting in the beginning. We solved these issues using data augmentation, dropout, and tuning the model properly.

Accomplishments that we're proud of

We successfully built a working AI system from start to finish. The model gives good accuracy and reduces the need for manual inspection. We are proud of turning a real-world problem into a practical AI solution.

What we learned

We learned how to build and train deep learning models, handle real-world data problems, and improve model performance. Most importantly, we learned how AI can solve real-life challenges.

What's next for AI Damage Detector

Next, we plan to add real-time detection, improve accuracy, and integrate object detection for better damage localization. We also aim to deploy it as a scalable cloud-based solution.

Built With

• ai
• and
• and-matplotlib-for-data-handling-and-visualization.-these-technologies-helped-us-build
• and-used-common-libraries-like-numpy
• built-with-we-built-ai-damage-detector-using-python-as-the-main-programming-language.-for-deep-learning-and-model-training
• deploy
• model
• pandas
• the
• train
• we-used-flask-to-create-a-simple-and-interactive-application-where-users-can-upload-images.-we-trained-and-tested-the-model-using-google-colab
• we-used-tensorflow-and-keras-along-with-opencv-for-image-processing.-for-the-web-interface