Vehicle Camera Blockage Detection Using Grid-Based Image Analysis
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Solution Overview
Problem
Camera blockage due to dirt accumulation, bird droppings, or objects obstructing the lens degrades image quality, rendering images unusable and requiring effective detection and response mechanisms.
Innovation Solution
A method and system that spatially and temporally analyze images using a partition grid to extract features, classify regions as blocked or unblocked, and apply smoothing techniques to generate an output signal for responding to blockages, such as cleaning the camera or disregarding affected images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple blockage detection methods are used, then device complexity is reduced, but measurement precision of blockage detection deteriorates
Solution Approach 1:
The image is divided into multiple regions using a partition grid, allowing blockage detection to be performed on specific regions rather than the entire image. This segmentation enables precise localization of blockages while maintaining computational efficiency by focusing analysis only on relevant areas.
Solution Approach 2:
The detection approach transitions from analyzing single images to analyzing sequences of images over time. By incorporating temporal dimension and examining changes across multiple frames, the system achieves more accurate blockage detection while managing complexity through efficient temporal feature extraction.
2Measurement precision
If detailed spatial and temporal analysis is performed, then measurement precision of blockage detection is improved, but loss of time for processing increases
Solution Approach 1:
The system extracts only the most relevant spatial and temporal features from image sequences rather than processing all image data. By identifying and analyzing key features such as region-specific changes and temporal patterns, the system achieves accurate blockage detection with reduced processing time.
Solution Approach 2:
The smoothing technique applies classification to more regions than strictly necessary, using a margin of analysis to ensure accurate blockage detection. This partial excess in analysis coverage improves detection reliability while the systematic approach keeps processing time manageable.
3Reliability
If smoothing techniques are applied to determine blocked regions, then reliability of blockage classification is improved, but device complexity increases
Solution Approach 1:
The partition grid is established and regions are predefined before actual blockage detection occurs. This preliminary structuring of the image into manageable regions simplifies the subsequent smoothing and classification operations, improving reliability without adding significant complexity during the detection phase.
Solution Approach 2:
The smoothing technique uses the classified regions and temporal sequences to automatically determine blockage status without requiring external intervention or complex post-processing. The system self-regulates the classification process by leveraging its own extracted features and temporal patterns.
Data Source
AI summary
A system for determining blockage of a vehicle camera is configured to apply a partition grid having a plurality of locations to a sequence of images to form a plurality of regions. The system determines at least one spatial feature corresponding to the partition grid and at least one temporal feature corresponding to the partition grid. The system generates a sequence of classifications for each location of the plurality of locations based on the at least one spatial feature, the at least one temporal feature, and reference information. The system applies a smoothing technique to determine a subset of regions that are blocked among the sequence of classifications, and generates an output signal based on the subset of regions. The output may be provided to an output system to wash the camera lens, notify the user or the vehicle of the blockage, or modify image processing.