The Damage Data endpoint returns a comprehensive report detailing detected vehicle damages, including severity levels, recommended repair actions, estimated costs for repairs, and an overall safety evaluation of the vehicle.

Key fields in the response include "damaged_parts" (listing each damaged part), "severity" (indicating damage level), "repair_action" (suggested repairs), "estimated_cost" (cost range for repairs), and "safety_evaluation" (overall safety assessment).

The response data is structured in a JSON format, with a main object containing arrays for "damaged_parts" and their respective attributes, allowing for easy parsing and analysis of each damage instance.

Users can customize their requests by providing parameters such as "vehicle_id," "damage_images" (for image uploads), and "inspection_type" (to specify the type of assessment), enhancing the accuracy of the analysis.

Typical use cases include assessing vehicle damage for insurance claims, evaluating repair needs for used vehicle inspections, and aiding repair shops in estimating costs and planning repairs efficiently.

Data accuracy is maintained through advanced automated analysis technologies that utilize machine learning algorithms, ensuring consistent and objective evaluations of vehicle damages based on extensive training data.

Users can expect standard patterns such as multiple damaged parts listed with varying severity levels, common repair actions like "replacement" or "partial repair," and a safety evaluation that indicates whether the vehicle is safe to drive.

Users can leverage the returned data to make informed decisions regarding repairs, budget planning, and safety assessments, facilitating communication between vehicle owners, repair shops, and insurance companies.

The API can detect various types of vehicle damages, including dents, scratches, deformations, misalignments, and more. Each damage type is assessed individually, allowing for detailed reporting on severity and recommended repair actions.

Users can customize requests by providing parameters such as "vehicle_id" to specify the vehicle being assessed, "damage_images" for visual analysis, and "inspection_type" to indicate the nature of the assessment, enhancing the accuracy of the results.

The returned data is structured in JSON format, featuring a main object that includes arrays for "damaged_parts" and their attributes, such as severity and repair actions, facilitating easy integration and analysis.

Key fields include "damaged_parts" (specific parts affected), "severity" (level of damage), "repair_action" (recommended repairs), and "safety_evaluation" (overall safety status), providing a comprehensive overview of the vehicle's condition.

Data accuracy is ensured through advanced machine learning algorithms that analyze extensive training data. Continuous updates and validation processes help maintain high-quality assessments and reliable results.

Users should check the input parameters for accuracy if results are partial or empty. Additionally, they can review the "damaged_parts" field to identify any detected damages and adjust their requests accordingly for more comprehensive analysis.

The API is designed to assess vehicle damages across various categories, including passenger cars, trucks, and SUVs. It is applicable in multiple regions, making it versatile for different automotive markets.

Typical use cases include insurance claim assessments, pre-purchase inspections for used vehicles, and repair cost estimations for automotive shops, enabling efficient decision-making and communication among stakeholders.

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