When performing image augmentation for object detection or segmentation, which rule must always be followed?

Question

BlackTom AI · Accepted Answer

Apply identical transformations to both image and labels because spatial correspondence between pixels and annotations must be preserved for object detection and segmentation.

Relevant knowledge points and concepts:
- Supervised detection/segmentation uses spatial labels (bounding boxes, keypoints, masks) that describe object locations in the image.
- Geometric augmentations (flip, rotate, scale, crop, translate) change pixel positions; labels must be transformed equivalently to remain correct.
- Class/category labels are invariant under geometry; spatial labels are not.

Step-by-step reasoning with an explicit example:
1. Suppose an image width W = 800 px and a bounding box with coordinates (x_min, y_min) = (100, 200) and (x_max, y_max) = (300, 400).
2. Apply a horizontal flip to the image. Pixel column x becomes x' = W - x.
3. Transform bounding box coordinates accordingly:
   - new_x_min = W - x_max = 800 - 300 = 500
   - new_x_max = W - x_min = 800 - 100 = 700
   - y coordinates remain the same: new_y_min = 200, new_y_max = 400
4. The updated bounding box (500,200)-(700,400) correctly locates the object in the flipped image. The same geometric flip must also be applied to segmentation masks (pixel-wise), keypoints (apply same transform to each point), and polygon annotations (transform each vertex).

Why the correct answer is correct:
- If labels are not transformed identically, the model receives inconsistent input–label pairs (e.g., object moved but label stays), causing the learning signal to be invalid and degrading performance.

Why other options are incorrect:
- "Apply augmentation only to the image": results in misaligned labels and incorrect supervision.
- "Apply augmentation only to labels": produces labels that no longer correspond to image pixels.
- "Randomize labels to improve robustness": randomizing labels breaks supervised learning and prevents the model from learning true object relationships.

Conclusion: The correct rule is to apply identical transformations to both image and labels, which matches the provided answer.

When performing image augmentation for object detection or segmentation, which rule must always be followed?单项选择题

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