Computer Vision Property Evaluation With Neural Network Feature Cropping
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Solution Overview
Problem
Existing property listing services provide limited and basic details, lacking specific information relevant to users, such as modernity of fixtures, natural lighting, and views, making it difficult for real estate agents to determine comparable properties and companies to generate targeted advertising.
Innovation Solution
Implementing a system that utilizes convolutional neural networks to identify and categorize property features through image cropping and classification, enabling detailed evaluations and targeted comparable property lists by training neural networks to recognize scenes and objects within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If basic property listing services are used, then service simplicity is maintained, but property information completeness deteriorates
Solution Approach 1:
The patent segments property evaluation into multiple specialized neural networks: a first NN for scene identification (kitchen, bathroom, living room), a second NN for quality assessment, and a third NN for modernity evaluation. Each network focuses on specific aspects of property features, enabling comprehensive information extraction without requiring a single complex system to handle all tasks simultaneously.
Solution Approach 2:
The system performs preliminary image processing and feature extraction before final property evaluation. Neural networks pre-identify scenes, crop relevant regions, and extract key features from property images beforehand, so that when property listings are generated, comprehensive information is already prepared and available for immediate use.
2Productivity
If manual property evaluation is performed, then evaluation accuracy is maintained, but processing efficiency deteriorates
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with automated neural network systems. The first neural network automatically identifies scenes and objects in property images, the second network assesses quality metrics, and the third network evaluates modernity. This substitution maintains or improves detection accuracy while dramatically increasing processing speed and productivity, enabling evaluation of numerous properties simultaneously.
Solution Approach 2:
The neural network system performs self-service property evaluation without human intervention. The networks automatically process property images, extract features, assess quality, and generate evaluations independently. This automation maintains consistent accuracy across all evaluations while eliminating the time and resource constraints of manual processing.
3Measurement precision
If detailed property analysis is implemented, then property feature detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides detailed property analysis into parallel processing streams using multiple specialized neural networks. Scene identification, quality assessment, and modernity evaluation are performed simultaneously by different networks rather than sequentially by a single system. This segmentation enables comprehensive detailed analysis while reducing total processing time through parallel computation.
Solution Approach 2:
The system performs preliminary cropping and pre-processing of property images before detailed analysis. By preparing images in advance and pre-identifying regions of interest, the neural networks can focus computational resources on detailed feature detection rather than initial image preparation, thereby improving detection accuracy without proportionally increasing processing time.
Data Source
AI summary
Systems and methods are disclosed for computer vision property evaluation. In certain embodiments, a method may comprise executing a computer vision property evaluation operation via a computing system. The computer vision property evaluation operation may include identifying an image of a selected property feature using a first neural network (NN) of the computing system, cropping the image of the selected property feature in a selected way to produce a cropped image using a second NN of the computing system, generating a categorization of the cropped image based on identified details of the selected property feature, and generating a classification of a property corresponding to the selected property feature based on the categorization.