Structure Face Detection Using Surface-Normal Clustered Bounding Frames
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Solution Overview
Problem
Existing object detection techniques, particularly in computer vision, face challenges with inaccurate detection and poor recall of structural objects such as tunnel entrances, building facades, and road signs, due to limitations in shape identification methods like single-pass principal component analysis (PCA).
Innovation Solution
A method involving a processor that receives data points describing a geographical area, classifies neighborhoods as linear, planar, or volumetric, merges clusters based on local surface normals, fits bounding frames to these clusters, and modifies them based on a field of interest, enhancing the detection and positioning of planar structural surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single-pass PCA based shape identification is used for object detection, then the detection process is simple and fast, but the detection accuracy and recall of structural objects are poor
Solution Approach 1:
The patent divides the detection process into multiple passes instead of using a single-pass approach. The first pass identifies candidate regions using PCA, while subsequent passes refine the detection by analyzing local surface normals and merging clusters. This multi-stage segmentation allows the system to maintain fast initial detection while improving accuracy through iterative refinement.
Solution Approach 2:
The patent performs preliminary classification of neighborhoods into linear, planar, or volumetric categories before final object detection. This preliminary action using PCA provides a rough but fast initial structure identification, which then guides more accurate but computationally intensive local analysis in subsequent steps, balancing speed and accuracy.
2Measurement precision
If multi-pass classification and clustering methods are used to improve detection accuracy, then the detection precision improves, but the computational complexity increases
Solution Approach 1:
The patent applies different levels of analysis to different regions of the data. Rather than uniformly processing all points with complex algorithms, it uses PCA for initial global structure identification and then applies more sophisticated local surface normal analysis only to candidate regions that require further examination. This local quality approach improves accuracy where needed while minimizing overall computational complexity.
Solution Approach 2:
The patent merges multiple detection results from different passes and clusters by combining information from local surface normals and spatial relationships. This merging process consolidates results from preliminary PCA-based detection with refined local analysis, achieving high detection accuracy while avoiding redundant computations by integrating rather than repeating analyses.
3Measurement precision
If detailed local surface normal analysis is performed for each neighborhood, then the classification accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments the processing into two distinct phases: a fast preliminary phase using PCA for rough classification, and a detailed refinement phase using local surface normal analysis only for candidate regions. This segmentation ensures that detailed analysis is applied selectively rather than uniformly, maintaining high classification accuracy while minimizing overall processing time by avoiding unnecessary detailed analysis of non-candidate regions.
Data Source
AI summary
An apparatus and method automatically detects and positions structure faces. After receiving data points describing a geographical area, neighborhoods are defined based on the data points and classified as linear, planar, or volumetric. Neighborhoods are merged into at least one cluster based on local surface normals. At least one bounding frame is fit to the at least one cluster and modified based on a field of interest.