Multi-Stage Object Classification Pipeline for Infrastructure Assessment
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Solution Overview
Problem
Traditional infrastructure maintenance and reliability assessments are time-consuming, labor-intensive, and fail to scale effectively for large numbers of components, relying on human operators with specialized knowledge.
Innovation Solution
A multi-stage object classification and condition pipeline using machine learning models for automated detection, classification, and condition assessment of infrastructure components, including localization, object classification, and object-condition classification, facilitated by a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators perform visual inspection and assessment of infrastructure components, then assessment accuracy can be maintained with specialized knowledge, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the mechanical visual inspection process performed by human operators with an automated image processing system using machine learning models. The system captures images of infrastructure components and uses trained models to automatically detect defects, classify objects, and assess conditions, eliminating the need for manual visual inspection while maintaining assessment accuracy through algorithmic analysis.
Solution Approach 2:
The system enables self-service assessment where the infrastructure components are automatically evaluated without human intervention. The machine learning models independently perform detection, classification, and condition assessment tasks that traditionally required specialized human expertise, allowing the system to serve itself in terms of automated decision-making.
2Reliability
If human operators with specialized knowledge assess infrastructure components, then reliable assessments can be obtained, but the process fails to scale effectively for large numbers of components
Solution Approach 1:
The patent creates a universal assessment system that can handle multiple types of infrastructure components (bridges, roads, buildings, etc.) through a single multi-functional platform. The machine learning models are designed to process various component types using the same core architecture, enabling the system to scale across different infrastructure domains without requiring separate specialized systems for each component type.
Solution Approach 2:
The system achieves scalability by changing the parameter of assessment capacity from limited human operator capacity to unlimited automated processing capacity. The machine learning models can process images and assess components at speeds far exceeding human capabilities, allowing the system to reliably assess large numbers of components simultaneously while maintaining consistent assessment quality through standardized algorithmic procedures.
3Reliability
If traditional manual inspection methods are used, then specialized human expertise can be applied, but the process requires high level of specialized knowledge and training
Solution Approach 1:
The patent extracts the specialized knowledge and expertise from human operators and embeds it within the machine learning models during the training phase. The models learn from labeled datasets containing examples of infrastructure components with various conditions, capturing expert knowledge in algorithmic form. This allows the system to retain assessment quality while eliminating the need for operators to possess specialized knowledge.
4Productivity
If automated machine learning systems are implemented, then scalability and speed are improved, but the system complexity increases with multiple models and stages
Solution Approach 1:
The patent segments the automated assessment system into distinct functional stages: image capture, object detection, object classification, and condition assessment. Each stage uses specialized machine learning models optimized for its specific task. This segmentation allows the system to process images through a structured pipeline where each component handles a specific function, improving overall efficiency while making the complexity manageable through modular design.
Solution Approach 2:
The system uses intermediate data structures and processing layers to connect different machine learning models. Detection models generate object proposals that serve as intermediaries for classification models, which in turn provide classified objects as inputs for condition assessment models. These intermediary representations facilitate efficient data flow between stages while reducing the computational complexity that would arise from direct end-to-end processing.
Data Source
AI summary
A system, method, and computer-program product includes detecting, via a localization machine learning model, a target object within target image data of a scene, classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes, routing, via the one or more processors, the target image data of the scene to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models based on a mapping between the plurality of distinct object classes and the plurality of distinct object-condition machine learning classification models, classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class of a plurality of distinct object-condition classes, and displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class.