Traffic Light Signal Detection Using HSV Brightness Thresholding
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Solution Overview
Problem
Existing object detection and classification systems in vehicles are processor-intensive, necessitating a reduction in processing requirements while maintaining accurate detection and classification of traffic light signals.
Innovation Solution
A computer-implemented method that receives a traffic light image, divides it into proceed-signal and stop-signal portions, determines brightness values using HSV color representation, and compares these values to thresholds to determine the illumination status of each portion, thereby reducing processing complexity and eliminating false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object detection and classification systems are used to detect traffic light signals, then detection capability is achieved, but processing requirements become excessive
Solution Approach 1:
The image processing task is segmented into distinct stages: initial image acquisition, region of interest identification (locating the traffic light), signal portion division (separating individual traffic light signals), and status determination. This segmentation allows each stage to use appropriately optimized algorithms, reducing overall processing requirements while maintaining detection accuracy
Solution Approach 2:
The method extracts only the relevant traffic light region from the entire scene image, then further extracts individual signal portions from the traffic light. By taking out and processing only the necessary portions rather than the entire image, processing requirements are significantly reduced while maintaining reliable detection
2Measurement precision
If image processing is performed on the entire traffic light image, then comprehensive analysis is achieved, but processing time and complexity increase
Solution Approach 1:
The traffic light image is divided into separate signal portions (red, yellow, green lights) and each portion is processed independently to determine its illumination status. This segmentation enables parallel processing of multiple signals and avoids unnecessary processing of dark or irrelevant regions, reducing processing time while maintaining precision
Solution Approach 2:
The method processes only the necessary signal portions that are relevant to traffic light status determination. By focusing computational resources on the actual light signals rather than the entire traffic light structure or surrounding areas, processing time is reduced without sacrificing measurement precision
Data Source
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AI summary
Aspects of the present invention relate to methods (60) of traffic light signal detection. In particular, an aspect of the invention relates to a computer-implemented method (60) of determining a status of a traffic light (111) in an image of the traffic light (111). The method (60) comprises: receiving an image of the traffic light (111); dividing the image of the traffic light (111) into at least two portions, including a proceed-signal portion (132) and a stop-signal portion (134); determining a first brightness value of a HSV colour representation of one of the proceed-signal portion (132) and the stop-signal portion (134); comparing the first brightness value to a first threshold; and identifying a first status of the traffic light (111) if the first brightness value is greater than or equal to the first threshold.