Computer Vision Object Detection and Pricing Automation
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Solution Overview
Problem
Current methods for object detection and classification in images and videos are time-consuming and prone to inaccuracies, requiring manual estimation by professionals like insurance adjusters, which leads to inefficient and unreliable insurance assessments.
Innovation Solution
A computer vision system utilizing convolutional neural networks (CNNs) for automatic detection, classification, and pricing of objects in images and videos, capable of real-time processing and generating pricing reports by comparing detected objects to a database and assigning fine-grained object codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual object identification and valuation is performed by professionals, then accuracy can be maintained through expert judgment, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of professional assessment with an automated computer vision system using convolutional neural networks. The CNN automatically detects, classifies, and prices objects in images, substituting human expert judgment with machine learning algorithms that process visual data rapidly while maintaining accuracy through trained models.
Solution Approach 2:
The system enables self-service automated assessment where the computer vision system independently performs object detection, classification, and valuation without requiring human intervention. The CNN processes images autonomously, generating pricing reports automatically, thus eliminating the need for manual professional assessment while maintaining efficiency and accuracy.
2Productivity
If automated computer vision systems are implemented for object detection, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional computer vision system where a single CNN-based platform performs multiple tasks: object detection, classification, and pricing. This universal system handles various object types and assessment scenarios through one integrated architecture, reducing the need for multiple separate systems while maintaining high processing speed and productivity.
3Adaptability or versatility
If manual estimation methods are used by professionals, then flexibility in handling unique cases is maintained, but consistency and reliability of assessments decrease
Solution Approach 1:
The patent utilizes parameter changes in the CNN model to adapt to different object types and assessment scenarios. The system adjusts its detection and classification parameters based on the specific characteristics of objects in images, maintaining flexibility in handling diverse cases while ensuring consistent and reliable assessments through standardized algorithmic processing rather than variable human judgment.
Data Source
AI summary
Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.