Convolutional Neural Network for Automated Object Pricing
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Solution Overview
Problem
Current computer vision systems require manual effort from professionals like insurance adjusters to detect, classify, and price objects in images or videos, which is time-consuming and prone to inaccuracies.
Innovation Solution
A computer vision system and method that uses convolutional neural networks to automatically detect, classify, and price objects in images or videos by comparing detected objects to a database and generating a pricing report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual object detection and classification is used, then accuracy can be maintained by professional judgment, but time consumption increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system using convolutional neural networks. The CNN model automatically detects, classifies, and prices objects in images, eliminating the need for manual professional judgment while maintaining accuracy through trained neural networks that process visual data systematically.
Solution Approach 2:
The system enables self-service automation where the computer vision model independently performs object detection, classification, and pricing without requiring human intervention. The trained neural network autonomously processes images, compares objects to databases, and generates pricing reports automatically.
2Adaptability or versatility
If manual object detection is used, then flexibility in handling complex scenes is maintained, but labor requirements and costs increase
Solution Approach 1:
The patent employs parameter changes by training convolutional neural networks with adjustable hyperparameters and using transfer learning techniques. The model can adapt to different scenes and object types by modifying network parameters and training data, enabling versatile handling of complex environments without requiring manual reconfiguration.
Solution Approach 2:
The system performs preliminary training and database construction before actual use. Pre-trained neural networks and comprehensive object databases are prepared in advance, allowing the system to immediately handle complex scenes during deployment without requiring on-site manual analysis or system reconfiguration.
3Productivity
If automated computer vision systems are used, then processing speed increases, but measurement accuracy may decrease due to algorithm limitations
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously refines its predictions by comparing detected objects against comprehensive databases and adjusting its classifications based on feedback from pricing verification and database updates. This iterative feedback loop maintains high accuracy while preserving fast processing speeds.
Solution Approach 2:
Extensive preliminary training using large datasets and pre-computed feature databases is performed before deployment. The convolutional neural networks are pre-trained on diverse object categories and scenarios, enabling the system to achieve high measurement precision automatically without requiring manual verification during actual use.
4Difficulty of detecting and measuring
If key point detectors are used, then object location identification is achieved, but the number of candidates increases requiring manual matching
Solution Approach 1:
The patent extracts and eliminates unnecessary key point matching operations by using pre-trained convolutional neural networks that directly process entire images to identify objects. Instead of extracting key points and manually matching them, the system extracts object information directly through automated CNN processing, removing the time-consuming matching step entirely.
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.