Side-View Camera Mask Boundary Reset for Vignetting Control
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Solution Overview
Problem
The vignette phenomenon in vehicle cameras mounted on side mirrors causes degradation of spatial recognition performance due to changes in the mask region, which is affected by side mirror operations or external impacts.
Innovation Solution
A vehicle system that automatically resets the mask region by generating edge and brightness difference maps from camera images, using edge detection, convolution, and weight application to determine the boundary between the vehicle and detection regions, and adjusts the mask region based on vehicle speed and steering wheel operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mask region is set to prevent vignette phenomenon degradation, then spatial recognition performance is improved, but the mask region changes due to side mirror operations or external physical impact, degrading recognition performance
Solution Approach 1:
The system performs preliminary actions by continuously capturing images and pre-calculating edge maps and brightness difference maps before mask region changes occur. These pre-computed maps are stored and ready to be used for rapid mask region resetting when changes are detected, preventing degradation before it happens.
Solution Approach 2:
The system implements feedback by monitoring changes in the mask region through continuous image processing. When a change is detected (through edge detection or brightness difference analysis), the system automatically resets the mask region based on the current images, creating a closed-loop control system that maintains spatial recognition performance.
2Measurement precision
If multiple images are processed to generate edge and brightness difference maps, then mask region accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary processing by continuously capturing images and pre-calculating edge maps and brightness difference maps even when mask region resetting is not immediately needed. This allows the processing to be spread out over time and ensures that when resetting is required, the computationally intensive work has already been done or is in progress.
Solution Approach 2:
The system uses periodic action by processing images at regular intervals or triggered by specific events (such as detecting vehicle speed changes or steering wheel operations). This periodic processing approach balances the need for accurate mask region determination with the constraint of processing time, as not all images require full processing.
3Measurement precision
If the mask region is adjusted based on vehicle operation conditions, then spatial recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies universality by using a single image processing framework that serves multiple functions: capturing images, generating edge maps, calculating brightness difference maps, detecting mask region changes, and resetting mask regions. This multi-functional approach reduces overall system complexity compared to having separate dedicated systems for each function.
Solution Approach 2:
The system implements self-service by automatically detecting changes in mask region and autonomously resetting it without requiring external intervention or complex control logic. The image processing system itself provides the service of mask region management, reducing the need for additional control system complexity.
Data Source
AI summary
Disclosed are apparatus and method for determining a mask region for a side field of view of a vehicle. A vehicle may include an image capturing device configured to acquire a plurality of images associated with a side field of view of the vehicle. The vehicle may generate, based on accumulated edge values, a first map and generate, based on accumulated brightness difference values, a second map, wherein the accumulated edge values and the accumulated brightness difference values are obtained by processing the plurality of images; determine a boundary between a vehicle region and a detection region in the side field of view based on: at least one first value for at least one pixel of the first map; and at least one second value for at least one pixel of the second map; and determine, based on the determined boundary, a mask region of the side field of view.