Autonomous Vehicle Camera Blockage Detection With Temporal Analysis
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Solution Overview
Problem
Autonomous driving systems face challenges in detecting camera field-of-view blockages, particularly when only a small portion is blocked, leading to inaccurate determinations and high false-positive rates due to limited information in single image frames.
Innovation Solution
An autonomous driving system that evaluates pixel values in captured images to identify candidate blocked regions, eliminates false positives by analyzing background and road surfaces, and uses temporal and spatial analysis to determine the likelihood of blockages, transmitting an alarm signal when a blockage is confirmed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-image blur detection mechanism is used, then detection simplicity is maintained, but blockage detection accuracy deteriorates and false-positive rate increases
Solution Approach 1:
The image is divided into multiple processing units (e.g., grid segments), and each unit is evaluated independently for blur characteristics. This segmentation allows the system to detect localized blockages more accurately while maintaining computational efficiency through parallel processing of discrete regions.
Solution Approach 2:
The detection approach transitions from analyzing a single image frame to analyzing multiple sequential image frames over time. By adding the temporal dimension, the system can distinguish between actual blockages (which persist across frames) and transient blur conditions (which appear and disappear), thereby reducing false positives while improving detection reliability.
2Productivity
If single-image blur detection is used, then processing speed is maintained, but false-positive rate increases due to limited information
Solution Approach 1:
The system pre-establishes blur detection thresholds and processing unit configurations based on historical data and statistical analysis. By preparing detection parameters in advance, the system can quickly evaluate incoming images without performing complex real-time calculations, thus maintaining high processing speed while ensuring reliable detection through pre-optimized criteria.
Solution Approach 2:
The detection system continuously processes sequential image frames, maintaining a running evaluation of blockage conditions. This continuous monitoring allows the system to leverage temporal patterns and persistency of blockages across multiple frames, improving reliability by distinguishing genuine blockages from transient artifacts while maintaining efficient processing through ongoing analysis.
3Measurement precision
If small portion blockages are detected, then detection sensitivity is improved, but false-positive rate increases due to limited distinguishable features
Solution Approach 1:
By dividing the image into fine-grained processing units, the system can detect blockages in small regions without being overwhelmed by the entire image complexity. Each segment is evaluated independently, allowing sensitive detection of localized obstructions while the segmented structure prevents false positives from propagating across the entire image.
Solution Approach 2:
The system uses temporal analysis across multiple image frames to verify small blockage detections. A genuine small blockage will persist across consecutive frames at the same spatial location, whereas false positives (such as transient blur or noise) will not show consistent temporal patterns. This temporal dimension provides the additional verification needed to maintain high sensitivity while reducing false positives.
Data Source
AI summary
Systems and methods for detecting blockages in images are described. An example method may include receiving a plurality of images captured by a camera installed on an apparatus. The method may include identifying one or more candidate blocked regions in the plurality of images. Each of the candidate blocked regions may contain image data caused by blockages in the camera's field-of-view. The method may further include assigning scores to the one or more candidate blocked regions based on relationships among the one or more candidate blocked regions in the plurality of images. In response to a determination that one of the scores is above a predetermined blockage threshold, the method may include generating an alarm signal for the apparatus.