Segmentation Artifacts
lastmod 2025-03-12created 2025-03-115 edits
An umbrella term for all visual distortion problems that occur during segmentation.
About
The term "Segmentation Artifacts" is mainly used in fields such as computer vision, image processing, and medical image analysis. Translated into Korean, it would be roughly "segmentation error" or "segmentation by-product".
Segmentation Artifacts are inaccurate or unwanted results produced while dividing an image or data into specific regions (segments), i.e., during segmentation. They appear when the algorithm misidentifies object boundaries, includes noise, or generates parts that do not match the actual data.
Causes
- Data quality issues: noise, low resolution, lighting changes, etc.
- Algorithm limitations: when the machine learning model is not trained enough or fails to learn complex patterns properly.
- Lack of post-processing: when the step of refining results after segmentation is skipped or poorly done.
Solutions
- Use better training data.
- Improve the segmentation algorithm (e.g., U-Net, DeepLab).
- Apply post-processing techniques (e.g., morphological operations, CRF).
Or
- Segmentation Artifacts → an umbrella term for all visual distortion problems that occur during segmentation.
- Deep Learning Upsampling Issues → suitable when mainly dealing with problems that occur during upsampling.
- Image Segmentation Errors & Artifacts → suitable for analyzing the various errors of segmentation models.
Jagged Edges
- The most common problem in deep learning segmentation models.
- When segmentation is done per pixel, boundaries are not smooth and look like stairs.
- Likely to occur when upsampling from low-resolution feature maps.
- Why the staircase effect occurs in RTMDet: resolution is not sufficiently preserved because masks are generated by upsampling low-resolution features.
📌 Related terms:
- Aliasing – distortion that occurs when converting a low-resolution image to high resolution.
- Pixelation – an image looking blocky due to low resolution.
- Staircase Effect – boundaries rendered like stairs instead of smoothly.
Examples
- RTMDet#Problem: staircase effect
- See Dongkuk Dangjin Rolling Mill#RTMDet Camber camera result review.
Checkerboard Artifacts (occurs with certain upsampling methods)
- Patterned distortion that appears depending on the upsampling method.
- Especially common when using Transpose Convolution(Deconvolution, Fractionally-strided Convolution).
- RTMDet's upsampling method may include this problem.
📌 Related terms:
- Upsampling Artifacts (upsampling noise) – distortion caused by data loss during upsampling.
- Deconvolution Artifacts – checkerboard patterns that appear when performing Deconvolution operations in a CNN.
Blurry Boundaries (including boundary blurring)
- In object-detection-based segmentation models such as RTMDet, boundaries may look blurry rather than sharp.
- Can occur with simple upsampling methods such as Bilinear Interpolation.
- May appear together with the staircase effect (rough boundaries or blurry boundaries).
📌 Related terms:
- Blurry Boundaries – when a deep learning model fails to clearly separate object boundaries.
- Interpolation Smoothing – when detail is lost while trying to smooth boundaries during upsampling.
Fuzzy Edge
- Examples of challenges for semantic segmentation. We show the original... | Download Scientific Diagram
- An end-to-end differential network learning method for semantic segmentation | Request PDF
In deep learning, a Fuzzy Edge refers to a state where the boundary is ambiguous or blurry (fuzzy) instead of being a clearly defined edge. It is generally used in image processing, computer vision, signal processing, and similar fields.