CarSegNet Uses Bounded Trimap Bands to Refine Auto Segmentation Mattes

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A developer working on CarSegNet, a vehicle image segmentation system, has detailed a runtime approach that restricts AI-based edge refinement to a narrow 'trimap band' around uncertain mask boundaries. The method prevents a neural refinement model from altering confidently segmented areas, addressing a common problem where improving one edge inadvertently damages another. The pipeline combines SAM for semantic masking with an NSJ alpha refiner that only operates within the uncertain strip, leaving the rest of the frame untouched. To handle cases where the prior mask is confidently wrong — such as missing antennas or filled wheel openings — the system adds an image-based edge term that widens the band where the photograph shows structure the mask does not. The design prioritizes cheap, inspectable cached-matte runs and avoids loading large segmentation checkpoints unless the execution path explicitly requires them.

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