Image Segmentation
Product Overview
Semantic Segmentation: Assigns category labels to every pixel in an image (roads, buildings, sky, vegetation, human figures, etc.).
Typical models: U-Net, DeepLab, SegFormer, etc.
Application scenarios: Autonomous driving perception, medical image segmentation, remote sensing analysis, industrial defect region localization.
Instance Segmentation: It executes category classification while differentiating distinct individual objects belonging to the same class (e.g., multiple pedestrians, multiple vehicles).
Typical models: Mask R-CNN, SOLO, YOLACT, etc.
Application scenarios: Robotic grasping, industrial sorting, security traffic/people counting, retail merchandise recognition.
Panoptic Segmentation:
Integrates semantic and instance segmentation branches: it generates instance masks for foreground objects and semantic labels for background zones.
Application scenarios: Autonomous driving, urban environment perception, high-accuracy scene comprehension.
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