Mask Edge Optimization Technical Solution
SegAnyMo provides a multi-level processing solution for edge jaggedness of output masks:
- SAM2 post-processing::
- Modify pred_iou_thresh in predictor.py in the sam2 directory (0.88-0.92 recommended)
- Enable -use_box_refine parameter for bounding box iterative optimization
- multimodal fusion::
- Combining the semantic features of DINOv2 (feature map in the dinos directory)
- Fusing depth information from depth_anything_v2
- Multimodal voting via core/utils/fusion.py
- morphological processing::
- Add morphologyEx operation to the output stage (requires changes to core/utils/vis_utils.py)
- Recommended 3×3 elliptical cores for expansion followed by corrosion
For particularly complex edges (e.g., hairs, transparent objects), it is recommended to 1) collect special data for fine-tuning; 2) increase the weight of edge_loss during training; and 3) manually label a small number of keyframes and then perform model adaptation via the -custom_train parameter.
This answer comes from the articleSegAnyMo: open source tool to automatically segment arbitrary moving objects from videoThe
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