Understanding Jaccard’s Index and Dice Coefficient in Object Detection and Image Segmentation

Object detection and image segmentation are essential tasks in computer vision, involving the identification and localization of objects within images. To evaluate the accuracy of these techniques, metrics like Jaccard’s Index and Dice Coefficient are used. Jaccard’s Index measures the degree of overlap between bounding boxes or masks, while Dice Coefficient quantifies the similarity between two masks. This article delves into the concepts and applications of these evaluation metrics.

In object detection, there are two distinct tasks:

• Determining whether an object exists in the image (classification)
• Fetching the location of the object (localization, a regression task).

In an image segmentation task, we also fetch the shape of the objects instead of just the bounding boxes around them.

Also, in a typical data set there are many classes, and their distribution is non-uniform (for example there might be many more peacocks than pigeons, and number of pixels of background are always much more than the number of pixels in the objects of our interest). It is important to keep a track of the risk of misclassifications. So, a simple accuracy-based metric will introduce bias.

So, we also need to associate a “confidence score” or model score with each bounding box/mask detected.

A word on Positives, TPs and FPs

(credits: https://stats.stackexchange.com/users/68057/gumeo)

In the case of image segmentation, let’s say that you have a mask with ground truth, let’s call the mask M. So, the mask has values 1 in the pixels where there is something you are trying to find and else zero. Now you have an algorithm to generate image/mask BB, which also has to be a binary image, i.e., you create a mask for your segmentation. Then we have the…