Vehicle Camera Image Correction for Windshield View Obstructions
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Solution Overview
Problem
Visual impairments in the field of view of vehicle cameras, such as contamination or heating elements on the windshield, lead to artifacts that compromise the reliability of computer vision algorithms, affecting safety and comfort in automated vehicle guidance systems.
Innovation Solution
Utilizing a second camera with a clear field of view to modify image data from a first camera with impairments through an artificial neural network, specifically a generative adversarial network, to remove the effects of visual obstructions and enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a first camera is used to capture images for vehicle guidance, then the system can operate with a single camera, but visual impairments (contamination, heating elements) degrade image quality and reliability
Solution Approach 1:
The patent uses a second camera as a copy of the first camera to capture the same environment. The second camera's clear image serves as a reference to correct the first camera's impaired image, allowing the system to maintain reliability without requiring the first camera to be perfectly clean or without adding complex cleaning mechanisms.
Solution Approach 2:
The patent introduces an intermediary processing system that combines images from both cameras. This intermediary system (involving image alignment, difference calculation, and neural network processing) mediates between the impaired first camera image and the clear second camera image to produce a corrected output, resolving the contradiction between simplicity and reliability.
2Reliability
If conventional image processing is applied to remove artifacts, then image quality may improve, but new artifacts are introduced and performance is limited
Solution Approach 1:
The patent replaces conventional mechanical/optical image processing methods with a neural network-based approach. Instead of using traditional algorithms that manipulate pixel data and introduce artifacts, the system uses a learned model (neural network) that has been trained on paired images to directly generate corrected images, eliminating the harmful artifacts of conventional processing.
Solution Approach 2:
The patent changes the fundamental parameters of image processing by using neural network transformations instead of traditional linear or geometric operations. The neural network learns complex non-linear mappings from impaired to clear images, fundamentally changing how image correction is achieved and avoiding the artifact problems of conventional parameter-based processing.
3Reliability
If a second camera is added to provide clear image data, then image reliability improves, but system complexity and cost increase
Solution Approach 1:
The patent makes the second camera serve multiple functions: it captures clear reference images for correction, provides backup data during impairment conditions, and enables the system to operate in various modes (single-camera mode when second camera is unavailable). This multi-functionality justifies the added complexity by providing versatile reliability improvement.
Solution Approach 2:
The patent implements a dynamic system that adapts between using only the first camera, using both cameras, or using the second camera exclusively based on the operational conditions. The system dynamically switches between different processing modes depending on whether the first camera is impaired, making the added complexity flexible rather than fixed.
Data Source
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AI summary
According to a method for guiding a vehicle (1), a first camera (2a) is used to generate a first image (7a), wherein an impairment (5) is present in a first field of view (3a) of the first camera (2a). A second camera (2b) is used to generate a second image data set (7b), and a computing unit (4) is used to generate input data depending on the first and the second image data set (7a, 7b). The input data is fed to a generator module (6a) and the first image data set is modified depending on the image data by the generator module (6a) to remove an effect of the impairment (5). The vehicle (1) is guided depending on the modified first image data set (7d).