Semantic Segmentation

CCTV Traffic Scene Semantic Segmentation Dataset

Definition

Semantic segmentation is the computer vision task of classifying every pixel in an image into a category, such as road, building, or pedestrian.

Purpose

The purpose is to provide detailed scene understanding for AI applications in self-driving, medical imaging, and robotics.

Importance

  • Essential for pixel-level perception in safety-critical systems.
  • Enables precise object boundaries compared to bounding boxes.
  • Requires large annotated datasets.
  • Computationally intensive at high resolutions.

How It Works

  1. Collect and label pixel-level annotated images.
  2. Train deep learning models like fully convolutional networks.
  3. Input image is processed into pixel-level predictions.
  4. Output mask assigns each pixel to a class.
  5. Evaluate with metrics like Intersection over Union (IoU).

Examples (Real World)

  • Cityscapes dataset: semantic segmentation for urban scenes.
  • Tesla Autopilot: pixel-level segmentation for road navigation.
  • Medical imaging: segmenting tumors in MRI scans.

References / Further Reading

  • Long et al. “Fully Convolutional Networks for Semantic Segmentation.” CVPR 2015.
  • Cityscapes Dataset.
  • IEEE Transactions on Medical Imaging.