Vehicle Camera Lens Contaminant Detection via Blob Analysis
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Solution Overview
Problem
Existing vehicle imaging systems face challenges in detecting and addressing dirt or contaminants on camera lenses, which can impair image quality and confuse machine vision algorithms, leading to incorrect object detection and potential safety issues during vehicle maneuvers.
Innovation Solution
A vision system that processes multiple frames of image data to detect blobs indicative of dirt or contaminants on the camera lens, using algorithms to determine the likelihood of contamination and generate alerts, and optionally trigger cleaning mechanisms or adjust image processing to compensate for lens impairments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vision system uses machine vision algorithms to process image data for object detection, then object detection capability is improved, but false positives occur when dirt or contaminants are present on the camera lens
Solution Approach 1:
The system performs preliminary detection of lens contaminants by analyzing image data for characteristic patterns (blobs, spots, or regions) that indicate dirt or water droplets on the lens surface. This preliminary action occurs before main object detection, allowing the system to identify and compensate for lens impairments in advance, thereby preventing false positives in subsequent detection operations
Solution Approach 2:
The vision system implements a feedback mechanism where detected lens contaminants trigger adjustments in image processing parameters or detection thresholds. When contaminants are identified, the system modifies its processing approach to account for the impaired image quality, creating a closed-loop system that continuously adapts to maintain reliable detection performance despite lens contamination
2Measurement precision
If the vision system processes multiple frames of image data to detect lens contaminants, then detection accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system applies partial action by selectively processing only those image frames or regions that show characteristics suggestive of lens contaminants. Rather than exhaustively analyzing every pixel in every frame, the system uses preliminary filtering and focused analysis on suspicious regions, achieving sufficient detection accuracy while reducing overall processing time and computational resources required
Data Source
AI summary
A vision system for a vehicle includes a camera having an image sensor and a lens, with the lens exposed to the environment exterior the vehicle. An image processor is operable to process multiple frames of image data captured by the camera and processes captured image data to detect a blob in a frame of captured image data. Responsive to processing a first frame of captured image data, and responsive to the image processor determining a first threshold likelihood that a detected blob is indicative of a contaminant, the image processor adjusts processing when processing subsequent frames of captured image data. Responsive to the image processor determining a second threshold likelihood that the detected blob is indicative of a contaminant when processing subsequent frames of captured image data, the image processor determines that the detected blob is representative of a contaminant at the lens of the camera.