Panoramic Vehicle Camera Image Correction Under Rain and Contamination
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Solution Overview
Problem
Existing camera-based vehicle systems struggle with degraded object and environment detection due to rain, incident light, or contamination, particularly in panoramic-view systems where cameras are outside the windshield wiper range, leading to restricted visibility and system failures.
Innovation Solution
A machine learning method using an artificial neural network to correct image data from multiple vehicle cameras by training on pairs of negatively influenced and clean images, determining a certainty measure for the correction, and outputting improved images with a controlled degree of correction based on a factor d, enhancing object recognition and visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If cameras are mounted outside the windshield wiper range (e.g., on A pillars or external mirrors), then the field of view for detection functions is improved, but visibility is restricted by raindrops or dirt that cannot be cleaned
Solution Approach 1:
The patent segments the image processing task into two parts: a neural network that detects and segments regions affected by raindrops, incident light, or dirt, and a separate correction module that processes only those affected regions. This allows the system to maintain the benefits of externally mounted cameras while algorithmically removing the negative effects of contamination.
Solution Approach 2:
The patent converts the harmful effect of raindrops and dirt into a detectable pattern that the neural network can identify. By training the network on images with known contamination patterns, the system learns to recognize these distortions and transform them into correctable data, effectively turning the harmful contamination into a detectable and correctable feature.
2Extent of automation
If the degree of automation is increased to SAE Level 4/Level 5, then more cameras are mounted on the outside of the vehicle, but the system becomes more vulnerable to contamination without cleaning possibilities
Solution Approach 1:
The patent implements a self-service system where the image processing unit automatically detects, analyzes, and corrects contamination effects without external intervention. The neural network continuously monitors incoming images for signs of raindrops, dirt, or incident light and applies corrections in real-time, allowing the system to maintain high automation levels despite external contamination.
Solution Approach 2:
The system employs feedback mechanisms where the output of the neural network (detected contamination regions) feeds into the correction module, which then produces corrected images. This closed-loop feedback ensures that contamination effects are continuously identified and corrected, maintaining system reliability at high automation levels.
3Measurement precision
If CNN-based methods are used for object recognition, then compensation for contamination is possible to a certain extent, but methods based on image features such as optical flow suffer severe degradation
Solution Approach 1:
The patent merges the strengths of CNN-based object recognition with traditional image feature analysis. By first using the neural network to detect and segment contamination-affected regions, and then applying correction techniques that preserve image features necessary for optical flow and structure from motion, the system achieves both accurate object recognition and robust feature-based processing.
Data Source
AI summary
The invention relates to correcting input image data, from a plurality of cameras of a vehicle panoramic-view system. includes: capturing input image data by the cameras, which are negatively influenced by rain, incident light and/or dirt, and providing to a trained artificial neural network, converting, by the trained network, the input image data into corrected output image data without negative influence, determining a certainty measure which is dependent on the degree of wetting by water, incident light and/or contamination for an image of the input image data, and characterizing the certainty of the trained network that the image correction of the network is accurate, and outputting, by the trained network, the output image data and the determined certainty measure. The method advantageously allows object recognition when cameras are fogged up and generation of an image data stream for human and computer vision from a network for an optimized correspondence search.