Supervised Defect Segmentation: Fine Defect Detection
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In this case, we aim to detect very small defects within the images.

.. image:: images/细小缺陷.png
        :scale: 50%


Accurate Supervised Defect Segmentation Model Test
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.. image:: images/细小准确训练.png

- The accurate supervised defect segmentation model was trained, validated, and tested on a dataset of 53 images.

Test results for the accurate supervised defect segmentation model:
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.. image:: images/准确检测OK.png

.. image:: images/准确检测NG.png

From the test results, we can see that the accurate supervised defect segmentation model struggles to detect very small defects.

Fast Supervised Defect Segmentation Model Test
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. image:: images/细小快速训练.png

.. image:: images/细小快速训练1.png

- The fast supervised defect segmentation model was also trained, validated, and tested on the same 53-image dataset.
  Additionally, a preprocessing step was applied to resize images to 1536 pixels.

Test results for the fast supervised defect segmentation model:
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.. image:: images/快速检测OK.png

From the test results, the fast supervised defect segmentation model is capable of detecting even very small defects.

**Conclusion**

- In this case, the fast supervised defect segmentation model demonstrated significantly better performance in detecting fine defects by using a preprocessing step to resize images.
- In practical deployment, the model automatically resizes the input image to the preprocessed resolution to ensure accuracy and consistency.
- Therefore, preprocessing is a key step in improving the performance of supervised defect segmentation models, especially for fine defect detection.