Industry Challenge: Operational Bottlenecks in Vehicle Risk & Damage Management

Vehicle inspection and damage assessment remain slow, inconsistent, and costly across insurance, rental, and fleet management sectors. Current methods often rely on:

• Time-consuming inspections

• Subjective human judgment and inconsistency

• Labor-intensive workflows and High operational costs

• Vulnerability to fraudulent or exaggerated claims

• Limited scalability across fleets and branches

As demand for fast, objective, and scalable damage detection grows, AI-powered Computer Vision becomes the key to modernizing vehicle risk and damage management. It is transforming how businesses handle these challenges. But building the vision capability is only part of the solution—what truly drives adoption is the ability to integrate, iterate, and deploy AI pipelines fast.

Tuba.AI: The AI Vision Workflow Builder, Not a System Integrator

At DevisionX, we don’t build complete car damage assessment systems. Instead, we provide Tuba.AI, a no-code/low-code platform purpose-built for designing, training, and deploying AI-powered computer vision pipelines.

This example use case illustrates how Tuba.AI can be used to create the AI Vision component of a vehicle inspection workflow. Whether your organization is building an insurance platform, a fleet monitoring tool, or a car rental return app—Tuba.AI helps you create the AI that powers it.

Use Case Overview: AI Vision Pipeline for Car Damage Detection

Tuba.AI allows any technical or non-technical team to create robust computer vision pipelines for tasks such as:

• Detecting dents, scratches, cracks, and broken parts

• Deploying models on edge devices or in the cloud

• Visualizing results with pixel-level overlays

• Integrating into existing inspection systems or mobile apps

Key Steps of the Workflow Built with Tuba.AI

In this use case, we used a car damage dataset to detect the damages in vehicles. 

1. Data Labeling:

   
   

   

   
   

   We uploaded a labeled dataset of vehicle images containing different types of damage (dent, scratch, crack, and broken). 

   
   

2. Model Training:

   
   

   

   
   

   We dragged and configured the Train Model block, selecting the training type as Segmentation,and set the mode to Manual to customize parameters.

   Alternatively, Automatic mode can be used for auto-training.

   • Accuracy vs Time: Set priorities between maximizing accuracy or reducing training time.

   • Framework Type: Choose PyTorch, the recommended framework due to its flexibility and strong community support.

   • OpenVINO/TensorRT format: Selected preferred model quantization (e.g., float16, float32, int8) to optimize model performance for deployment.

   • Accuracy: Selected Optimum Accuracy to ensure the model generalizes well across various types of cat damage.

   • Time: Defined the maximum duration for training based on project needs.

   Training successfully completed with built-in performance tracking for continuous monitoring and evaluation.

   
   

3. Deployment:
   Once trained, the model was seamlessly deployed on cloud infrastructure or edge devices, enabling real-time car-damage segmentation from images or video feeds. 

   
   

4. Inference and Performance Evaluation:
   The deployed model processed new vehicle image samples, generating segmented damage masks in real time. Tuba.AI’s dashboard facilitated monitoring of model predictions, confidence levels, and performance metrics, helping refine accuracy and reduce false positives.

   
   

5. Integration and Scalability:

   
   

   

   
   This damage segmentation workflow, built with Tuba.AI is ready to integrate smoothly into systems such as:

   • Insurance claim processing platforms

   • Car rental return systems

   • Fleet management tools

   • Auction house inspection systems

Results of the Use Case

During model training on the car damage dataset:

• Initial mAP (mean Average Precision): Near 0% — indicating the expected low starting point before the model learned damage patterns

• Middle-stage mAP: Around 4-9% — showing gradual improvement as the model began recognizing basic damage features

• Peak Training mAP: Approximately 17% — reaching its highest performance point in the middle of training

• Final Validation mAP: Around 13-14% — stabilizing at this level by the end of the training process

  
  

The trained model demonstrated strong potential in detecting diverse types of car damage—across varying vehicle makes, models, and damage severities. While still in the early stages, the results highlight a solid foundation that, with further refinement, can evolve into a reliable AI-powered component within larger vehicle inspection or claims processing systems.

In visual tests, the model accurately identified various car damages across different vehicles. 

This showcases Tuba.AI’s ability to generate robust, adaptable, and production-ready AI vision models that generalize well across real-world environments—proving the platform’s effectiveness in building scalable AI pipelines for damage detection and beyond.

Business Impact for Car Rental Operators and Insurance Providers

Advantages of Tuba.AI for Industry players

Tuba.AI empowers teams to rapidly build, train, and deploy the AI Vision layer of their damage detection systems, without needing to write code or manage complex infrastructure.

No-Code AI Vision Platform:
Empower your non-technical team to build and manage car damage detection models—no coding or ML expertise needed.

• Fast End-to-End Pipeline:

  Go from images to a working AI model in days with built-in auto-labeling, one-click training, and streamlined deployment.

  
  

• Real-Time Edge Performance:

  Deploy AI models on edge devices like return kiosks, mobile tablets, or car-mounted cameras for on-the-spot inspections.

  
  

• Simple Scaling & Updates:

  Easily add new damage types or retrain models—no need to rebuild your workflow from scratch.

  
  

• Seamless Integration:

  Deploy anywhere—cloud, edge, or on-prem—with standard formats like OpenVINO, ONNX, and TensorRT.

  
  

• Faster ROI, Less Complexity

  Skip long AI development cycles and start saving time and cost across your claims, rental, or fleet operations.

Tuba.AI: Powering the AI Vision Layer Behind Smarter Damage Assessment

Tuba.AI isn’t just another AI tool, it’s a workflow builder designed to help businesses rapidly develop, test, and deploy AI-powered computer vision pipelines without coding or AI expertise.

Whether you’re digitizing vehicle inspections, streamlining claims processing, or modernizing rental returns, Tuba.AI enables your team to build the AI vision component—faster, smarter, and ready to integrate into your existing systems.

Ready to accelerate your damage detection workflows with AI?

Let Tuba.AI help you build the vision layer that powers it all. Talk to our expert now.

Resources:

Car Damage Segmentation