Automated Facility Deficiency Detection via Sensor Data
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Solution Overview
Problem
Existing methods for identifying deficiencies in public facilities, such as crosswalks and traffic lights, are inefficient and often require human inspection, leading to delayed maintenance and potential safety hazards.
Innovation Solution
A computer-implemented system that monitors live sensor data from cameras and sensors to detect abnormal user behavior, determining facility deficiencies and executing mitigating actions automatically, such as generating reports or adjusting traffic light timings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspection methods are used to identify facility deficiencies, then measurement precision can be maintained, but productivity is reduced and response time increases
Solution Approach 1:
The patent replaces manual human inspection with an automated computer vision system using cameras and machine learning algorithms. The system captures images of facilities, processes them through trained neural networks, and automatically identifies deficiencies such as cracked sidewalks or damaged crosswalks, eliminating the need for physical human inspection while maintaining detection accuracy.
Solution Approach 2:
The facility inspection system performs self-diagnosis by automatically detecting and reporting its own observations without human intervention. The machine learning model continuously monitors facilities, autonomously identifies anomalies, and generates reports, enabling the system to serve itself in the inspection task.
2Device complexity
If manual inspection processes are used, then device complexity remains low, but loss of time increases due to delayed detection and response
Solution Approach 1:
The system performs preliminary detection by continuously monitoring facilities in real-time, identifying deficiencies as they occur or develop. This advance detection capability allows authorities to respond immediately to safety issues before they escalate, eliminating the time delay inherent in periodic manual inspection schedules.
Solution Approach 2:
By replacing slow manual inspection processes with automated optical sensing and rapid image processing, the system dramatically reduces the time required to detect and report facility deficiencies while managing complexity through software-based solutions.
3Productivity
If automated sensor monitoring is implemented, then productivity and response time improve, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional integrated system where a single platform performs multiple tasks: image capture, real-time processing, deficiency detection, and report generation. This universal system handles various facility types (sidewalks, crosswalks, roads) and multiple deficiency categories, reducing overall system complexity compared to having separate specialized systems for each function.
Solution Approach 2:
The system merges previously separate functions—manual inspection, photography, analysis, and reporting—into a single automated workflow. By combining camera systems, machine learning models, and reporting mechanisms into one integrated platform, the system achieves high productivity while managing complexity through unified architecture.
Data Source
AI summary
A computer-implemented method comprising: monitoring, by a computing device, live sensor data received from one or more sensor devices; detecting, by the computing device, abnormal behavior of one or more individuals or objects based on the monitoring the live sensor data; determining, by the computing device, a deficiency of a facility based on the detecting the abnormal behavior; and executing, by the computing device, a computer-based instruction based on the deficiency of the facility.