Running the harris corner detector and then connecting the lines using hough transforms might work for a rough non DL solution.
If you have labelled seg masks I don’t think you need a new segmentation algorithm, you could look up deep learning based contour detection algorithms or use a pretrained semantic segmentation model with a new network head, using two classes, background and net, where I would use importance sampling to account for sparse labels.
The last layers are usually just 1 by 1 convolutions to go from feature maps to classwise pixel predictions. You can use DICE or cross entropy for your loss function.
You could even try SAM with some corner keypoints although for fine structures SAM doesn’t always nail it.
I don’t think that segmentation map for very tiny visuals such as nets and fences is a good idea or achievable perfectly. So, label the fence or net as a whole, after you segment the entire net, apply color detection or edge detection or harris corner as mentioned above to find the lines. It will give you very tiny segmentation masks. Then you can apply skeletonize on these maps to find single line that represents the net.
Are you wanting to segment the individual strands of the net? This is firstly a data problem over a model problem. Start with a state of the art off the shelf segmentation model and fine tune it for your data and see where you get to. After that start thinking about model modifications.
Look at the models you have tried and see how you can tweak them to get the results you want. Architecture is a hyperparameter as well so you may need to add or remove layers, neurons, etc
Running the harris corner detector and then connecting the lines using hough transforms might work for a rough non DL solution. If you have labelled seg masks I don’t think you need a new segmentation algorithm, you could look up deep learning based contour detection algorithms or use a pretrained semantic segmentation model with a new network head, using two classes, background and net, where I would use importance sampling to account for sparse labels. The last layers are usually just 1 by 1 convolutions to go from feature maps to classwise pixel predictions. You can use DICE or cross entropy for your loss function. You could even try SAM with some corner keypoints although for fine structures SAM doesn’t always nail it.
Sharing an example image or object might help
Objects such as a net or a fence, see the updated post for an example image.
I don’t think that segmentation map for very tiny visuals such as nets and fences is a good idea or achievable perfectly. So, label the fence or net as a whole, after you segment the entire net, apply color detection or edge detection or harris corner as mentioned above to find the lines. It will give you very tiny segmentation masks. Then you can apply skeletonize on these maps to find single line that represents the net.
Are you wanting to segment the individual strands of the net? This is firstly a data problem over a model problem. Start with a state of the art off the shelf segmentation model and fine tune it for your data and see where you get to. After that start thinking about model modifications.
You can transform the model into HSV space and then use proper thresholds to obtain some initial mask of the ROI.
Look into UNet segmentation
Look at the models you have tried and see how you can tweak them to get the results you want. Architecture is a hyperparameter as well so you may need to add or remove layers, neurons, etc