Facility Signage Anomaly Detection With Spatial Impact Mapping
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Solution Overview
Problem
Existing systems fail to promptly detect and report anomalies in facility signage such as missing, damaged, or illegible signs, which can lead to health and safety hazards and commercial losses.
Innovation Solution
A computer-implemented method using image analysis and natural language processing to detect and map signage, identify anomalies, and determine impact areas, with geo-timestamped recording and notification of changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of signage is performed, then detection accuracy can be maintained, but detection speed and response time are significantly reduced
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system that uses image processing algorithms to detect signage anomalies. The system captures images of facility areas, automatically processes them to identify missing, damaged, or illegible signs, and generates reports without human intervention in the detection process.
Solution Approach 2:
The system enables self-service anomaly detection by autonomously monitoring signage conditions throughout the facility. The computer vision system independently performs detection, analysis, and reporting functions, freeing personnel from manual inspection tasks while maintaining continuous monitoring capability.
2Reliability
If comprehensive signage monitoring is implemented across the entire facility, then anomaly detection coverage is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent divides the facility into multiple discrete areas or zones, each monitored by specific image capture devices. The system processes images area by area, mapping signage to specific locations within the facility layout. This segmentation allows comprehensive coverage while managing computational complexity through localized processing.
Solution Approach 2:
The system performs preliminary actions by pre-mapping signage locations to facility areas before anomaly detection begins. A baseline map of expected signage is created and stored, allowing the system to quickly compare current images against known configurations without re-analyzing entire facility datasets during each detection cycle.
3Loss of time
If real-time anomaly detection and reporting is implemented, then response time to hazards is reduced, but computational processing requirements increase
Solution Approach 1:
The patent extracts and focuses computational resources on detecting specific anomaly types rather than performing exhaustive analysis of all signage features. The system identifies key characteristics such as sign presence, damage patterns, and legibility issues, processing only the essential data needed for anomaly detection while ignoring redundant information.
Solution Approach 2:
The system implements feedback mechanisms where detected anomalies are immediately reported and can trigger alerts to facility personnel. The comparison between current and baseline signage maps provides continuous feedback on facility conditions, enabling rapid response to hazards while optimizing processing through pattern recognition from previous detections.
Data Source
AI summary
Methods, computing devices, and computer-readable storage media are provided. A computing device receives an image of a respective area of a facility, detects signage within the image, and maps the detected signage to the respective area represented in a map to produce a map of signs that is stored in a spatial data structure. Natural language processing with references to well-known symbols semantically understands information included on the signage, which is then stored. When one or more anomalies are determined to exist in the signage, information regarding the one or more anomalies is presented. An impact area of the signage is determined, if any. The received image is compared with a previously received image of the respective area to determine a difference. The computing device presents an indication of the difference.