Aerial Image Segmentation for RF Node Placement
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Solution Overview
Problem
In pre-sales situations for refinery wireless planning, the lack of detailed plant data hinders the accurate estimation and placement of RF nodes, as existing methods require three-dimensional modeling which is not feasible without such data.
Innovation Solution
A novel aerial image segmentation framework using readily available two-dimensional data from sources like Google Maps to approximate RF node placement by segmenting regions into tank farms, vegetation, and process areas, employing image processing techniques, human visual system features, and Delaunay triangulation for polygon formulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional modeling is used for RF propagation modeling, then accuracy of RF node placement is improved, but data requirements increase (detailed plant data is needed)
Solution Approach 1:
The patent uses two-dimensional aerial images as a simplified copy or representation of the actual three-dimensional plant environment. Instead of requiring detailed 3D models, the system processes 2D aerial imagery to extract spatial information about tanks, vegetation, and process areas, which is then used to estimate RF node placement. This copying approach maintains reasonable accuracy while significantly reducing data requirements.
2Measurement precision
If detailed plant data is collected for three-dimensional modeling, then RF propagation model accuracy is improved, but time and cost increase
Solution Approach 1:
The patent employs readily available, low-cost two-dimensional aerial images from sources like Google Maps instead of investing time and resources in collecting detailed plant data for three-dimensional modeling. These 2D images serve as sufficient input for the segmentation framework, enabling rapid estimation of RF node placement without the time-consuming process of gathering and processing detailed plant specifications, terrain data, and structural information.
3Measurement precision
If three-dimensional modeling is performed, then RF node placement accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential spatial information needed for RF node placement from aerial images, rather than creating comprehensive three-dimensional models. The segmentation framework identifies and extracts key features such as tank farms, vegetation areas, and process areas from 2D imagery, discarding unnecessary complexity while retaining sufficient detail for accurate RF propagation modeling and node placement estimation.
Data Source
AI summary
A system receives a two-dimensional digital image of an aerial industrial plant area. Based on requirements of image processing, the image is zoomed in to different sub-images, that are referred to as first images. The system identifies circular tanks, vegetation areas, process areas, and buildings in the first image. The system formulates a second digital image by concatenating the first images. The system creates one or more polygons of the regions segmented in the second digital image. Each polygon encompasses a tank area, a vegetation area, a process area, or a building area in the second digital image, which is a concatenated image of the individual regions. The system displays the second digital image on a computer display device.