Attachable Matter Detection Using Histogram Analysis
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Solution Overview
Problem
Existing in-vehicle camera systems face challenges in accurately detecting attachable matters like raindrops due to blurred images and erroneous detections of similar shapes, leading to reduced visibility assistance and sensing accuracy.
Innovation Solution
An attachable matter detection apparatus that acquires and analyzes photographic images by creating histograms of edge intensity, luminance, and saturation, determining the presence of attachable matters based on frequency ratios and exclusion conditions, and employing partitioned areas to improve detection accuracy and reduce processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If edge detection algorithm is used to detect attachable matter, then detection speed is improved, but detection accuracy deteriorates due to blurred contours
Solution Approach 1:
The invention changes the detection parameters from simple edge detection to multi-dimensional histogram analysis. By creating histograms of edge intensity, luminance, and saturation with multiple grades, the system captures more nuanced characteristics of attachable matter, improving detection accuracy without sacrificing speed.
Solution Approach 2:
The invention transitions from two-dimensional edge detection to three-dimensional histogram space analysis. By analyzing the distribution of edge intensity, luminance, and saturation across multiple grades simultaneously, the system detects attachable matter through their characteristic distribution patterns rather than relying solely on contour clarity.
2Productivity
If simple contour detection is used, then processing load is reduced, but erroneous detections increase due to similar shapes
Solution Approach 1:
The invention introduces exclusion conditions based on histogram characteristics to filter out false positives. By analyzing the specific distribution patterns of edge intensity, luminance, and saturation, the system distinguishes attachable matter from similar-shaped objects, improving detection reliability while maintaining processing efficiency.
Solution Approach 2:
The invention uses histogram distribution patterns as feedback to validate detection results. The exclusion conditions act as a feedback mechanism that confirms whether detected contours truly represent attachable matter by checking their characteristic histogram signatures, reducing erroneous detections.
Data Source
AI summary
An attachable matter detection apparatus according to an embodiment includes an acquirement unit, a creation unit, and a determination unit. The acquirement unit acquires a determination target area of an attachable matter from a photographic image. The creation unit creates histograms of at least an edge intensity, luminance, and saturation for the determination target area acquired by the acquirement unit. The determination unit determines whether or not the attachable matter exists in the determination target area on the basis of a ratio of frequency of each grade in each of the histograms created by the creation unit.