Raindrop Detection Edge Rejection for Windshield Wiper Control

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Solution Overview

Problem

Existing raindrop detection methods for vehicle windshields require significant computation time and memory resources, making them inefficient for real-time processing and automatic windscreen wiper control.

Innovation Solution

A method that focuses on rejecting uncharacteristic edges in captured images by selecting closed, elliptic, or round edges with specific grey level variations, using connected component analysis and geometric criteria, and tracking these components across frames to distinguish raindrops from other windshield features without excessive calculation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If comprehensive image processing techniques are used to detect raindrops, then detection accuracy is improved, but computation time and memory resources increase significantly

Engineering Contradiction:
Improveraindrop detection accuracyVSAvoidcomputation time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the raindrop detection process into distinct stages: edge detection, connected component analysis, geometric feature extraction, and classification. Each stage processes only relevant features, reducing overall computational burden while maintaining detection accuracy through systematic feature analysis.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent applies different processing strategies to different regions of the image. By focusing computational resources on detecting closed edges and analyzing geometric properties only in relevant areas, the system achieves high detection accuracy without processing the entire image at full resolution, thus reducing computation time.

Inventive Principle:
Principle #3Local quality

2Measurement precision

If comprehensive image processing techniques are used to detect raindrops, then detection accuracy is improved, but memory resources increase significantly

Engineering Contradiction:
Improveraindrop detection accuracyVSAvoidmemory resources
Core Design Contradiction:
Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts only the essential features needed for raindrop detection: closed edge properties, geometric characteristics, and grey level variations. By extracting and analyzing only these critical features rather than processing the entire image data, the system maintains high detection accuracy while significantly reducing memory resource requirements.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The detection process is segmented into feature extraction and classification stages, where only relevant features are extracted and stored for analysis. This segmentation allows the system to maintain accurate detection by preserving essential feature data while discarding redundant information, thereby optimizing memory usage.

Inventive Principle:
Principle #1Segmentation

3Productivity

If simple edge detection is used, then computation time is reduced, but inability to distinguish raindrops from other features reduces detection accuracy

Engineering Contradiction:
Improveprocessing speedVSAvoidraindrop detection accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The patent enhances simple edge detection by applying local quality analysis through connected component analysis and geometric feature extraction. This allows the system to process edges efficiently while adding discriminative features (closed loops, specific shapes, grey level patterns) only where needed, maintaining high processing speed while improving the ability to distinguish raindrops from other features.

Inventive Principle:
Principle #3Local quality

Solution Approach 2:

The patent changes the analysis parameters from simple edge presence to multiple discriminative parameters including geometric properties, connectivity, and grey level variations. This parameter transformation enables the system to maintain computational efficiency while significantly improving detection accuracy through multi-parameter classification.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS9230189B2Method of raindrop detection on a vehicle windscreen and driving assistance device
Publication Date: 2016.01.05 VALEO SCHALTER & SENSOREN GMBH

AI summary

The invention relates to a method of raindrop detection on a vehicle windscreen by capturing images using a camera which is at least focused on the windscreen, including a step of detecting edges (102) in a research area of said captured images, characterized in that said method of raindrop detection comprises a rejection step (103) in which edges that are uncharacteristic with respect to a raindrop are rejected. This invention also relates to an associated driving assistance device.