Home Façade Feature Detection Using CNNs for Area Quantification
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Solution Overview
Problem
Existing technologies lack efficient methods for detecting and quantifying exterior features of houses from remote sensing imagery, particularly in identifying and measuring features like doors, windows, and façades, and classifying material conditions such as damage or need for repair, using machine learning techniques.
Innovation Solution
A system utilizing Convolutional Neural Networks (CNN) to analyze street-level and aerial imagery, cropping images to focus on individual houses, and converting pixel data to terrestrial measurements, enabling detection and quantification of exterior features and their conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used to identify exterior features, then measurement precision can be maintained, but productivity is severely limited
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system using Convolutional Neural Networks (CNNs) to detect and quantify exterior features. The system processes images automatically to identify features such as doors, windows, and façades, converting pixel data to real-world measurements. This substitution enables high-speed processing of multiple properties simultaneously while maintaining measurement precision through algorithmic accuracy and automated pixel-to-dimensional conversion.
2Productivity
If automated image processing is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional automated system that performs multiple tasks within a single integrated framework: image acquisition, preprocessing, feature detection using CNNs, quantification of exterior features, and conversion of pixel measurements to real-world dimensions. The system can analyze various feature types (doors, windows, façades) and material conditions (damage, repair needs) simultaneously, reducing overall system complexity despite the advanced processing capabilities.
3Measurement precision
If comprehensive feature detection is performed, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent applies preliminary image preprocessing steps including cropping to focus on individual property exteriors and enhancing image quality before feature detection. By preparing images in advance with appropriate cropping and enhancement, the system reduces the computational burden during the actual feature detection phase, enabling comprehensive analysis of multiple features with high precision while minimizing processing time through optimized workflow sequencing.
Data Source
AI summary
A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.