Bounding-Box Precision/Recall for Empty Images

1

I have difficulties with calculating bounding-box recall and precision for a validation set that contains roughly 50/50 images with objects and images without objects.

As recall can’t be calculated for images without any objects, I use these images only for getting a better estimate of my precision:

precision = P_p * N_p / (N_p + N_n)
recall = R_p

where P_p, R_p are the precision and recall values for non-empty images, N_p is the number of predicted bounding boxes for non-empty images and N_n is the number of predicted bounding-boxes for empty images.

Do you see any issues with this?
Are there any papers available that address this issue?

This forum really needs LaTeX integration!

2

Hi! I don’t think that there are any issues with your approach. That’s generally how it’s done to the best of my knowledge. For papers, look at the Pascal VOC AP, COCO mAP

3

Oh another thing I’ve been meaning to try is to calculate some other metrics for a greater model understanding.

  1. Average IoU for all predicted boxes (wrong predictions give a IoU of 0)
  2. Average IoU for correct predicted boxes

And if you haven’t seen it, there is a github repo that can help with bounding box metrics.