Traffic Light Illumination Recognition Using Backlight Segmentation
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Solution Overview
Problem
The recognition of traffic light colors is challenging in bright sunlight due to backlight interference, leading to inaccurate image recognition.
Innovation Solution
A method utilizing a processor and camera system to capture images, apply deep learning models for traffic light detection, and employ a segmentation network to identify the relative positions of traffic lights, converting RGB to HSV color space to extract illumination regions based on brightness thresholds, allowing accurate recognition of traffic light colors despite backlight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image recognition technology is used to recognize traffic light colors, then traffic light state detection is enabled, but recognition accuracy deteriorates in bright sunlight due to backlight interference
Solution Approach 1:
The patent applies segmentation by dividing the traffic light detection process into multiple stages: initial traffic light detection, segmentation map generation, illumination region extraction, and color recognition. The segmentation map separates the illumination region from the background, effectively isolating the traffic light signal from backlight interference and improving recognition accuracy in bright sunlight conditions
Solution Approach 2:
The patent introduces a segmentation map as an intermediary element between the original image and the color recognition process. This segmentation map serves as a mediator that highlights the illumination region while suppressing background interference, enabling accurate traffic light color detection even in challenging backlight conditions
2Measurement precision
If standard image recognition is applied without segmentation, then processing is simpler and faster, but recognition precision deteriorates under backlight conditions
Solution Approach 1:
The patent implements segmentation by generating a segmentation map that divides the image into relevant and irrelevant regions. This segmentation process, while adding computational steps, significantly improves measurement precision by isolating the traffic light illumination region from the complex backlight background, making the additional complexity necessary for achieving accurate color recognition
Solution Approach 2:
The patent applies local quality by focusing processing resources on the illumination region identified in the segmentation map. Instead of processing the entire image uniformly, the system concentrates analysis on the specific region containing the traffic light signal, improving precision while managing complexity through selective processing
Data Source
AI summary
A vehicle-borne method for recognizing the illumination state of traffic lights even against a backlighting of strong sunlight or other light source obtains a first image of a set of traffic lights in a road traffic environment. A segmentation map is acquired by dividing a first region from the first image, and an illumination region in the segmentation map is extracted by marking RGB pixels in the region which are of a preset threshold in brightness according to a training model. A lit color of the set of traffic lights is recognized according to a position of the illumination region in the segmentation map. By utilizing the method, accuracy of recognition of illumination state of traffic lights is improved.