Mirrorless Car Side Image Processing for Limited Display Recognition
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Solution Overview
Problem
Mirrorless cars with limited display apparatuses face challenges in recognizing objects due to image clarity issues, especially with varying light conditions, increasing the risk of accidents.
Innovation Solution
A lateral image processing method and apparatus that recognizes areas of different image change, adjusts image clarity based on the car's driving state, and emphasizes boundary lines between areas of significant and minor image change to enhance object recognition on a limited display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a limited size display apparatus is used in a mirrorless car, then the device complexity is reduced, but the object recognition capability deteriorates due to insufficient display area
Solution Approach 1:
The display apparatus is segmented into multiple regions corresponding to different driving states (driving region, parking region, charging region). Each region displays optimized image content tailored to specific driving conditions, allowing effective utilization of limited display area while maintaining recognition capability across various scenarios
Solution Approach 2:
Different clarity enhancement techniques are applied to different regions of the displayed image based on local characteristics. Areas with significant image changes (potential hazards) receive enhanced clarity processing, while stable background areas maintain normal display, optimizing recognition without requiring increased display size
2Measurement precision
If image clarity is enhanced for object recognition, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The system performs preliminary analysis of the captured image to identify regions with significant changes (potential objects or hazards) before applying clarity enhancement. This pre-identification allows selective processing only of critical areas, reducing overall processing time while maintaining recognition accuracy for important objects
Solution Approach 2:
Instead of applying clarity enhancement to the entire image, the system applies partial enhancement only to identified regions of interest. This partial action approach achieves sufficient recognition accuracy for critical objects without the computational overhead of processing the complete image, thereby reducing processing time
3Measurement precision
If the display apparatus size is increased to improve object recognition, then the object recognition capability improves, but the device complexity and cost increase
Solution Approach 1:
The system changes parameters of the displayed image (clarity, contrast, brightness) dynamically based on driving state and detected object characteristics. By optimizing these visual parameters, the system achieves effective object recognition on limited-size displays without requiring physical enlargement of the display apparatus
4Measurement precision
If image processing is applied to enhance clarity in all areas, then the object recognition improves, but the processing complexity increases
Solution Approach 1:
The image processing is segmented into distinct stages: region identification, clarity enhancement, and boundary emphasis. Each stage processes specific aspects of the image independently, reducing overall processing complexity compared to applying a single comprehensive enhancement algorithm to the entire image
Data Source
AI summary
The present invention relates to a lateral image processing method, and the method includes recognizing areas in which amounts of image change are different from each other in an image captured by a camera; determining whether a car is in a driving state; and converting an image in one area in which an amount of image change is different according to the driving state of the car.