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DAMAGE ANALYSIS FOR THE INSURANCE INDUSTRY
Manual, inefficient, and slow vehicle condition assessment when conducted in-person. Developing an efficient AI-driven system for assessing and estimating vehicle damages in insurance claims processing.
The company leveraged the power of LinkedAI’s image labeling services to annotate and categorize various types of vehicle damages, ranging from minor scratches to major collisions. Segmentation annotation was used to outline and classify different damage types accurately.
The AI-powered damage assessment system streamlined the insurance claim process, reducing manual effort and improving accuracy in assessing the extent of vehicle damages.Decreased claim processing time by 40% and increased accuracy in damage assessment by 20%, leading to quicker claims resolution and enhanced customer satisfaction.
Developing an AI system for accurate pedestrian detection in various urban environments.
LinkedAI provided high-quality image labeling services to annotate pedestrians in different scenarios, including crowded streets, crosswalks, and intersections. LinkedAI’s team used a combination of bounding boxes and semantic segmentation to precisely outline pedestrians and their context.
The AI model demonstrated a significant reduction in false negatives and false positives, resulting in a safer pedestrian detection system. Increased pedestrian detection accuracy by 20%, leading to improved safety and enhanced user trust.
OBJECT RECOGNITION FOR URBAN DRIVING
Enhancing AI capabilities to recognize and respond to various objects encountered during urban driving, such as traffic signs, parked vehicles, and construction barriers.
The company used LinkedAI’s image labeling services to annotate a wide range of objects encountered in complex urban environments. This company employed polygon annotations, and bounding box annotation to provide detailed information about object types and their attributes.
decision-making during urban driving scenarios. Achieved a 25% increase in object recognition accuracy, contributing to a more reliable and efficient urban driving experience.
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