Vehicle Camera Obstruction Detection With ROI Persistence Analysis
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining the integrity of camera sensor data due to environmental obstructions such as dirt, debris, or other materials, which can degrade image quality and affect the vehicle's functionality.
Innovation Solution
A system that identifies and classifies obstructions in the camera's field of view by segmenting image frames into regions of interest, using a classifier to determine clear or obstructed areas, and generating a function request to trigger cleaning cycles or alert operators when obstructions persist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vehicle operates in environmental conditions with dirt, debris, or other materials, then the vehicle can continue moving and performing functions, but the camera image quality degrades due to obstructions
Solution Approach 1:
The system performs preliminary detection of obstructions on the camera lens by analyzing image frames and identifying regions with abnormal characteristics (e.g., uniform color, low variance, specific texture patterns). When obstructions are detected, the system proactively triggers cleaning actions before the obstructions severely degrade image quality and affect vehicle functionality, thus maintaining reliability while ensuring operational continuity
Solution Approach 2:
The system continuously monitors camera image quality by processing image frames through analysis modules that detect obstruction patterns. The detection results provide feedback to the cleaning system, which adjusts its operation accordingly - triggering cleaning when obstructions are detected and stopping when the field of view is clear, thereby maintaining a balance between operational continuity and image quality
2Measurement precision
If the system continuously monitors image frames to detect obstructions, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system divides the image frame into multiple regions of interest (ROIs) and processes each region independently using parallel computation. The image processing module segments frames into overlapping or non-overlapping regions, allowing the system to detect obstructions in specific areas without analyzing the entire frame in detail, thus improving detection accuracy while reducing overall computational energy consumption
Solution Approach 2:
The system applies partial processing by focusing computational resources only on regions where obstructions are likely to occur or where abnormal patterns are detected. Instead of uniformly processing the entire image frame with high computational intensity, the system selectively applies detailed analysis to specific ROIs, achieving sufficient detection accuracy with reduced energy consumption
Data Source
AI summary
A system such as an autonomous vehicle's perception system will identify and classify an obstruction in a field of view of an image capturing device. The system will receive a sequence of image frames from the image capturing device. For each of the image frames, the system will segment the image frame into a regions of interest (ROIs), and the system will use a classifier to assign a classification to each ROI. The classification indicates whether the ROI is clear or obstructed. The system will aggregate the classifications for each ROI to determine an aggregate classification. When an obstructed classification persists for a threshold number of image frames, the system will classify the image capturing device as obstructed, and it will generate a function request that, when executed, will cause a system of which the image capturing device is a component to perform a function.