CATV Tap Auditing via Drone Imagery and ML
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Solution Overview
Problem
Traditional CATV system audits are labor-intensive, dangerous for technicians, and limited in effectiveness due to the need for physical inspection of CATV taps from utility poles.
Innovation Solution
The use of drones to capture images of CATV taps, combined with machine learning to identify the taps and determine the connection status of coaxial cables, and access to subscriber information databases to verify authorized use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technicians perform physical inspection of CATV taps from utility poles, then inspection accuracy is improved, but technician safety deteriorates
Solution Approach 1:
The patent uses drones to capture images of CATV taps and coaxial cable connections, creating visual copies that can be analyzed without technicians physically accessing the taps. The image processing system analyzes these copies to identify connections and determine authorization status, eliminating the need for technicians to climb utility poles while maintaining inspection accuracy.
Solution Approach 2:
The patent replaces the mechanical system of physical inspection with an automated image-based system. Drones equipped with cameras capture images, and machine learning algorithms automatically analyze the images to identify coaxial cable connections and determine tap authorization status, substituting manual physical inspection with automated optical and computational methods.
2Measurement precision
If technicians perform physical inspection of CATV taps, then connection status can be determined, but labor intensity and time consumption increase
Solution Approach 1:
The patent implements an automated system where drones autonomously navigate to CATV tap locations, capture images, and the machine learning system automatically processes the images to determine connection status and authorization. The system serves itself by eliminating the need for human technicians to perform manual inspection, thereby reducing labor intensity and increasing audit efficiency.
Solution Approach 2:
The patent replaces manual physical inspection with automated image capture and machine learning analysis. The system automatically processes images to identify coaxial cable connections and determine tap authorization status, significantly reducing the time and labor required for audits compared to traditional manual inspection methods.
3Object-affected harmful factors
If ground-based inspection methods are used, then technician safety is improved, but inspection effectiveness and accuracy deteriorate
Solution Approach 1:
The patent transitions from ground-based two-dimensional inspection to aerial three-dimensional imaging. Drones capture images from elevated positions, providing superior viewing angles and closer proximity to CATV taps without requiring technicians to physically access utility poles. This dimensional change enables both enhanced safety and improved inspection effectiveness.
Data Source
AI summary
A computing system receives at least one first image of a first device affixed to a structure above ground. The computing system determines that the at least one first image depicts a CATV tap comprising a plurality of coaxial connectors. The computing system determines, based in part on the at least one first image and a set of coaxial cables, each coaxial cable connected to a coaxial connector of the plurality of coaxial connectors, that a use status of the CATV tap is an authorized use status or an unauthorized use status. The computing system performs an action based on the use status.