Horizon-Based Image Segmentation for Vehicle Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques for autonomous vehicles face challenges in recognizing objects due to homogeneous textures, leading to errors as objects blend with the background, especially below the horizon line in images.
Innovation Solution
The method analyzes only the parts of the image above the horizon line, reducing computational workload and error likelihood by selecting objects that are more likely to stand out from their background, using a camera's pitch to determine the horizon line and processing images to determine distances to objects above this line.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire image is processed to identify objects, then object detection coverage is improved, but computational complexity and processing time increase
Solution Approach 1:
The image is segmented into multiple regions based on the horizon line, with different processing strategies applied to regions above and below the horizon. This segmentation allows the system to focus computational resources on critical areas while reducing overall processing complexity.
Solution Approach 2:
The horizon line is extracted as a key reference feature from the image, and processing is extracted selectively based on the region. By taking out the horizon as a reference and processing only relevant regions, the system reduces computational load while maintaining detection effectiveness.
2Reliability
If the entire image is processed to identify objects, then detection completeness is improved, but processing time increases
Solution Approach 1:
The image processing is segmented into distinct regions (above and below horizon) with different processing priorities. This segmentation enables parallel processing and reduces the sequential processing time while maintaining detection completeness in critical regions.
Solution Approach 2:
The system applies partial processing to certain image regions based on their importance. By performing selective processing on regions above the horizon with higher priority and reduced processing on other regions, the system achieves acceptable detection completeness with reduced processing time.
3Productivity
If image processing focuses on regions below the horizon line, then processing speed is improved, but object recognition accuracy deteriorates due to homogeneous textures
Solution Approach 1:
Different processing qualities and strategies are applied to different local regions of the image. Regions above the horizon receive enhanced processing with higher quality analysis to compensate for homogeneous textures, while regions below the horizon receive standard processing, optimizing both speed and accuracy locally.
4Productivity
If only parts of the image above the horizon line are processed, then computational efficiency is improved, but object detection coverage may be reduced
Solution Approach 1:
The image is segmented into regions above and below the horizon line, with differentiated processing strategies. This segmentation allows the system to maintain high computational efficiency while ensuring adequate detection coverage in both regions through appropriate processing allocation.
Solution Approach 2:
The system performs partial processing on the image by focusing intensive analysis on regions above the horizon while applying lighter processing to regions below. This partial action approach maintains computational efficiency while preserving necessary detection coverage through selective processing intensity.
Data Source
AI summary
A method is provided for processing an image in which only parts of the image that appear above a point on a horizon line are analyzed to identify an object. In one embodiment, the distance between the object and a vehicle is determined, and at least one of the speed and direction of the vehicle is changed when it is determined that the distance is less than the range of a sensor. The method for processing images is not limited to vehicular applications only and it may be used in all applications where computer vision is used to identify objects in an image.