Instance segmentation is a powerful annotation technique that not only identifies each object in an image at the pixel level (like semantic segmentation) but also distinguishes between multiple instances of the same class. For example, instead of labeling all cars as one class, each car is uniquely segmented. This enables AI models to count, separate, and analyze individual objects with extreme accuracy.
• Segmenting overlapping cells, tumors, or organ tissues individually
• Analyzing patient scans for diagnosis and research
• Creating high-resolution datasets for AI-assisted medical imaging
• Differentiating between multiple cars, pedestrians, bikes, etc.
• Crucial for real-time decision-making and collision avoidance
• Creating layered datasets for perception and planning modules
• Separating individual products in cluttered retail shelves
• Enhancing computer vision in automated checkout systems
• Product detection and SKU-level analytics
• Isolating each fruit, leaf, or crop segment for precision farming
• Analyzing disease spread at plant-level granularity
• Drone-based segmentation of farmlands at per-plant resolution
• Part-level recognition in complex machinery and products
• Assembly verification and defect detection
• Multi-object tracking in automated workflows
• Mapping individual buildings, vehicles, trees, or land patches
• Urban planning and land-use categorization
• Disaster impact analysis with per-object breakdowns
