Vessel Shape Analysis for Manned vs Unmanned Classification
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Solution Overview
Problem
Existing systems struggle to accurately distinguish between manned and unmanned vessels in real-time imagery, particularly under adverse conditions, leading to potential misclassification and ineffective engagement strategies.
Innovation Solution
A system utilizing shape analysis techniques, including elastic shape analysis and path straightening algorithms, to identify and classify objects of interest by comparing their shapes to reference shapes, while incorporating a scale factor to differentiate between manned and unmanned vessels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shape analysis techniques are used to distinguish between manned and unmanned vessels, then classification accuracy is improved, but the complexity of the detection system increases
Solution Approach 1:
The system segments the vessel detection problem into distinct shape analysis components: extracting vessel轮廓 (contour), comparing against reference shapes, and applying classification rules. This segmentation allows complex classification to be achieved through multiple simple, manageable steps rather than a single complex algorithm.
Solution Approach 2:
Instead of having the system learn complex patterns through machine learning, the patent inverts the approach by using predefined reference shapes of known vessel types and comparing detected vessels against these references. This reverse approach simplifies the system while maintaining accuracy.
2Speed
If real-time image processing is performed to identify objects of interest, then response time is improved, but processing accuracy deteriorates under adverse conditions
Solution Approach 1:
The system extracts only the essential shape information from images, removing unnecessary data such as color, texture, and background details. By taking out only the critical shape features and comparing them against reference shapes, the system achieves both real-time processing and accurate classification even in adverse conditions.
3Measurement precision
If scale factor analysis is incorporated to differentiate between vessel types, then object identification accuracy is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system changes the parameter being measured from complex multi-dimensional vessel characteristics to a single scale factor parameter. By comparing the size of detected vessels against reference shapes and determining if they fall within expected scale ranges, the system improves identification accuracy while simplifying the measurement process.
Data Source
AI summary
Embodiments relate to a system for determining when an object should be an object-of-interest. An input module receives an image scene captured by an image capture device. A processor performs a shape analysis technique, wherein a first processing module scans the image scene to identify an object, a second processing module identifies a shape of interest of the object, a third processing module compares the shape of interest to a reference shape, and a fourth processing module classifies the object as having a target characteristic when the shape of interest matches the reference shape. The processor compares an interior area of an image curve of the classified object to an interior area of an image curve of the reference shape to generate a scale factor, and designates the classified object as an object-of-interest based on the scale factor.