Construction Object Detection Using Brightness and Color Channels
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying and distinguishing construction objects in real-time, as existing perception systems often fail to provide the type of object, which is crucial for safe maneuvering, and require extensive data processing and specific detectors for various types of construction objects.
Innovation Solution
A method and system that convert camera images into brightness and color channels, using templates and classifiers to identify potential construction objects by detecting bright orange regions and stripes, allowing for robust detection of various construction objects without requiring specific detectors for each type, and enabling the vehicle to process images in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing perception systems use specific detectors for various types of construction objects, then detection accuracy for each object type may be improved, but device complexity and processing requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single multi-functional detection system that can identify various construction objects (cones, barrels, signs, barriers) using one detector. The system converts images to brightness and color channels and uses template matching with classifiers to detect different object types, eliminating the need for multiple specialized detectors while maintaining detection accuracy across diverse construction objects.
2Reliability
If existing perception systems process extensive data to identify construction objects, then detection reliability may be improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies the extraction principle by isolating and processing only the critical features needed for construction object detection. The system extracts brightness and color channel information from images, then applies template matching and classifiers specifically designed for construction objects. This selective extraction of relevant data reduces computational overhead and processing time while maintaining detection reliability.
3Adaptability or versatility
If the system converts images into multiple channels and uses templates with classifiers, then the ability to detect various construction objects is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: converting the image to brightness and color channels, applying template matching, and then using classifiers to identify specific construction objects. This segmented approach allows the system to handle diverse construction objects through a structured multi-step process, improving versatility while managing computational complexity through organized processing steps.
Data Source
AI summary
Aspects of the disclosure relate to identifying construction objects. As an example, an image captured by a camera associated with a vehicle as the vehicle is driven along a roadway may be received. This image may be converted into a first channel corresponding to an average brightness contribution from red, blue and green channels of the image. The image may also be converted into a second channel corresponding to a contribution of a color from the red and the green channels of the image. A template may then be used to identify a region of the image corresponding to a potential construction object from the first channel and the second channel.