Bright
Understand how overexposed or excessively bright images affect dataset quality and how to detect and correct them using Visual Layer.
What Are Bright Images and Objects?
Bright images are overexposed visuals with elevated brightness levels that wash out details and distort colors. These typically occur due to excessive lighting, reflective surfaces, or misconfigured camera settings.
Common Causes of Brightness Issues
- Excessive lighting conditions: Strong sunlight, intense indoor lighting, or flash can overexpose parts of an image.
- Reflective surfaces: Shiny objects like glass, metal, or water reflect excessive light and may create bright hotspots.
- Camera settings: A high ISO, wide aperture, or slow shutter speed can result in the image sensor capturing too much light.
Why It Matters
| Problem | Impact |
|---|---|
| Loss of detail | Overexposure reduces contrast and texture, especially in highlights. |
| Color distortion | Washed-out areas may appear inaccurate or completely white, affecting labeling or recognition. |
| Lower model performance | Vision algorithms may struggle to detect or segment bright objects due to low edge visibility. |
| Visual quality degradation | In public-facing use cases, poor exposure impacts perceived quality and credibility. |
How to Detect and Remove Bright Images
-
Detect Bright Images:
Go to “Add Filter” → “Quality Issue” → select “Bright” → choose “IS” as the logic operator → set the desired confidence threshold (default is 0.5).
Export the bright images using Export → “Matching the applied filter.” -
Correct Bright Images:
Go to “Add Filter” → “Quality Issue” → select “Bright” → choose “IS NOT” as the logic operator → set the desired confidence threshold (default is 0.5).
Export the images without bright issues using Export → “Matching the applied filter.”
Improving brightness balance ensures your dataset retains visual clarity, maintains labeling accuracy, and supports better-performing models.