Traffic light segmentation refers to the process of isolating and identifying the individual traffic light signals from a video or image stream. This is crucial in automated systems such as self-driving cars, where the accurate detection of traffic light states (red, yellow, green) is necessary for decision-making. The primary goal is to enhance the robustness and accuracy of these systems by enabling real-time recognition of traffic lights under varying environmental conditions.

Key steps in traffic light segmentation:

  1. Preprocessing the input image to enhance features.
  2. Applying segmentation techniques to isolate traffic light areas.
  3. Classifying the detected signals based on color and shape.

Important considerations:

The performance of segmentation algorithms heavily depends on the quality of the input data, lighting conditions, and the angle of view of the camera.

Common segmentation methods include:

  • Thresholding techniques.
  • Deep learning-based methods, such as convolutional neural networks (CNNs).
  • Edge detection algorithms like Canny and Sobel filters.

The final output should be a clear identification of the traffic light and its corresponding state, ensuring safe and efficient navigation for autonomous vehicles.