Survey Marker Detection Using Concentric Luminance Segmentation
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Solution Overview
Problem
Accurate detection of survey markers from captured images is challenging due to issues like color expansion, contraction, and pattern similarity with soil or snow, leading to reduced detection accuracy in building and soil volume measurements.
Innovation Solution
An image processing apparatus and method that extracts candidate regions from captured images of survey markers with concentric circles of varying radii and hues, using feature extraction and discrimination units to accurately identify the markers, regardless of orientation and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple survey marker pattern is used, then the marker is easy to capture, but detection accuracy decreases due to color expansion, contraction, and pattern similarity with soil or snow
Solution Approach 1:
The survey marker is divided into multiple concentric circular regions with different luminance values (first through fourth circular regions). This segmentation creates a unique multi-zone pattern that distinguishes the marker from natural backgrounds like soil or snow, while maintaining a simple circular overall shape that is easy to capture and identify.
Solution Approach 2:
Different regions of the marker have different luminance characteristics (some regions are brighter, others darker). This local variation in quality creates a distinctive pattern that enhances detectability and accuracy, allowing the system to differentiate the marker from uniform-colored backgrounds while preserving the simple circular geometry for easy capturing.
2Adaptability or versatility
If environmental conditions vary (lighting, weather), then the survey marker can be captured in different settings, but detection accuracy decreases due to interference from soil, snow, and changing luminance
Solution Approach 1:
The marker's segmented concentric circular structure with alternating luminance zones creates a unique pattern signature that remains distinguishable from environmental backgrounds (soil, snow) under varying lighting and weather conditions. The pattern's internal structure provides reference points for accurate detection regardless of external interference.
Solution Approach 2:
The marker utilizes luminance variations (brightness changes) across different circular regions to create a detectable pattern. This luminance-based encoding allows the marker to maintain detectability under varying environmental lighting conditions, as the relative brightness patterns remain consistent even when overall lighting changes.
3Device complexity
If a conventional single-color or simple pattern marker is used, then the marker design is simple, but detection accuracy is reduced due to similarity with natural backgrounds
Solution Approach 1:
The marker maintains a simple circular overall shape and can be implemented as a flat plate or signboard, but internally it is segmented into concentric circular regions with different luminance values. This provides a unique multi-zone pattern that distinguishes it from natural backgrounds while preserving design simplicity and ease of manufacturing.
Solution Approach 2:
The marker uses local luminance variations within different circular regions to create a distinctive pattern. This approach enhances detection accuracy by creating contrast with uniform natural backgrounds, while the overall simple circular design and flat plate construction maintain design and manufacturing simplicity.
Data Source
AI summary
There is provided a survey marker, and an image processing apparatus, method, and program capable of accurately detecting a survey marker from a captured image obtained by image capturing. The survey marker has a planar shape and includes a plurality of circles concentrically disposed, the plurality of circles including adjacent circles each having a different color. A candidate region extraction unit extracts a candidate region from a captured image obtained by image capturing of the survey marker, the candidate region being a candidate of a region in which the survey marker appears. A feature amount extraction unit extracts a feature amount of the candidate region. A discrimination unit discriminates the survey marker on the basis of the feature amount. The present technology can be applied to, for example, a case of detecting a survey marker installed on the ground from a captured image obtained by aerial image capturing.