Ensuring Data Variability for Better Generalization

In real-world applications, variability in images is inevitable due to changes in lighting, positioning, or slight differences in manufacturing processes. Incorporating variability in the training set is essential for improving a CNN’s robustness.

Ways to Ensure Data Variability

• Include Images from Different Conditions: Capture images with varied lighting, angles, and settings to represent realistic scenarios.
• Utilize Augmentation: Apply transformations like scaling, rotations, and brightness adjustments to simulate different conditions.

Example in Automotive Part Inspection

In automotive manufacturing, parts may be inspected in different lighting conditions. Adding slight brightness and contrast adjustments to the training images ensures that the CNN performs reliably under all inspection conditions, reducing the chance of poor detection in specific lighting scenarios.