Computer Vision Power Line Hazard Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems for detecting power line hazards from imagery require onsite manual inspections and are computationally expensive, making them inefficient for accurately determining vegetation proximity to power lines and generating risk reports.
Innovation Solution
A computer vision system that generates digital surface models, vegetation masks, and 3D power line models from imagery, identifies sections of power lines at risk from vegetation, and produces vegetation risk reports, including location, distance, and severity of risk, without manual inspections, using a combination of digital surface models, vegetation masks, and digital difference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision systems are used to process aerial images and generate 3D surface models, then three-dimensional modeling capability is achieved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential elements needed for vegetation-risk assessment (power line positions, vegetation locations, and their spatial relationships) rather than generating complete 3D surface models of entire areas. This selective extraction maintains necessary measurement precision while significantly reducing computational cost by avoiding unnecessary processing of non-relevant geographic data.
Solution Approach 2:
The system segments the image processing task into specific functional components: detecting power lines, detecting vegetation, calculating distances, and assessing risks. This segmentation allows each component to be processed independently with optimized algorithms, reducing overall computational burden while maintaining accuracy for the specific vegetation-risk assessment application.
2Measurement precision
If manual inspection is performed to determine vegetation proximity to power lines, then accurate risk assessment is achieved, but time consumption and labor costs increase
Solution Approach 1:
The system enables self-service automated detection where computer vision algorithms automatically identify power lines, vegetation, and their spatial relationships without human intervention. The system processes images, calculates distances, and generates risk assessments autonomously, eliminating the need for manual field inspections while maintaining measurement precision through algorithmic analysis.
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated computer vision system that uses image processing and algorithms to detect vegetation proximity to power lines. This substitution eliminates human labor and time consumption associated with manual field inspections while maintaining or improving measurement accuracy through consistent automated analysis.
3Loss of information
If comprehensive 3D modeling of all structures is performed, then complete geometric information is obtained, but system complexity and processing requirements increase
Solution Approach 1:
The system applies local quality by focusing computational resources only on areas and elements relevant to vegetation-risk assessment (power lines and surrounding vegetation) rather than modeling all structures in the scene. This approach maintains complete geometric information for critical elements while reducing system complexity by ignoring non-essential structures.
Solution Approach 2:
The system performs preliminary identification and filtering of relevant objects (power lines and vegetation) before conducting detailed geometric analysis. This preliminary action separates critical elements from non-critical ones, allowing the system to maintain complete geometric information for risk-assessment-relevant objects while avoiding the complexity of modeling unrelated structures.
Data Source
AI summary
Computer vision systems and methods for detecting power line hazards from imagery are provided. The system obtains at least one image from an image database having an object and/or a structure present therein, and generates a digital surface model (DSM), a vegetation mask, and a three-dimensional (3D) power line model based on the obtained image. The system generates a digital line model based on the 3D power line and a risk distance indicative of a distance between the 3D power line and vegetation proximate to the 3D power line, and also generates a digital tree model. The system generates a digital difference model based on an intersection of the digital line model and the digital tree model, identifies sections of a power line within a risk distance of vegetation located proximate to the power line based on the digital difference model, and generates a vegetation risk report based on the identified sections of the power line, the predetermined 2D risk distance, a predetermined 3D risk distance, and an elevation risk distance.