Exploring 2D Instance Semantic Segmentation

2D instance semantic segmentation is an advanced computer vision technique that combines aspects of both semantic segmentation and instance segmentation. While semantic segmentation assigns a class label to each pixel in an image (e.g., road, vehicle, pedestrian), instance segmentation goes further by distinguishing between individual objects of the same class. This means not only knowing which pixels are "cars," but identifying specific cars as separate entities (Car 1, Car 2, etc.).

Why is it Important?

Instance semantic segmentation provides a more detailed and comprehensive understanding of scenes than simple object detection or basic segmentation. This is crucial for applications where you need to:

• Precisely identify the boundaries of individual objects
• Track specific objects across frames or images
• Understand complex scenes with multiple overlapping objects of the same class
• Create more precise training data for computer vision models
  
  

Use Cases

• Autonomous Driving: Enables precise detection of vehicles, pedestrians, lane markings, and navigable areas
• Infrastructure Analysis: Facilitates traffic sign recognition, parking management, and road condition assessment
• Interior Sensing: Analyzes vehicle cabin environments and tracks occupant activities
• Quality Control: Validates annotations by comparing predictions with ground truth and identifies performance issues
  
  
  

What to Look for in a 2D Segmentation Solution

Selecting the right technology for 2D instance semantic segmentation requires careful consideration of its features. Advanced annotation tools are necessary, including polygon-based segmentation for exact object boundaries, feature locking to secure completed annotations, and multi-layer support for assigning several class values to a single pixel. Integration with modern solutions such as the Segment Anything Model (SAM) significantly speeds up workflow efficiency. Equally important is the strength of the platform’s workflow: the ability to synchronize 2D segmentation with 3D bounding boxes enables true multi-sensor fusion, while the capacity to track objects across image sequences and export results in standard industry formats supports seamless operations. Quality assurance is fundamental and must include tools for comparing model outputs with annotations, dataset searching and filtering by segment size, and visualization capabilities for quickly spotting discrepancies or ambiguous cases.

Getting Started

To get started with 2D instance semantic segmentation using Kognic:

1. Contact Kognic to discuss your specific project requirements
2. Set up your annotation project by defining your taxonomy (classes and attributes)
3. Upload your images to the Kognic platform
4. Use Kognic's annotation tools with features like SAM and tracking to create your segmentation annotations
5. Leverage Kognic's exploration tools to validate and improve your dataset quality

Want a closer look at the process? Our product expert Adrian has made a concise video showcasing the entire 2D instance semantic segmentation workflow: