  Object detection or localization is an incremental step in progression from
coarse to fine digital image inference. It not only provides the classes of the
image objects, but also provides the location of the image objects which have
been classified. The location is given in the form of bounding boxes or
centroids. Semantic segmentation gives fine inference by predicting labels for
every pixel in the input image. Each pixel is labelled according to the object
class within which it is enclosed. Furthering this evolution, instance
segmentation gives different labels for separate instances of objects belonging
to the same class. Hence, instance segmentation may be defined as the technique
of simultaneously solving the problem of object detection as well as that of
semantic segmentation. In this survey paper on instance segmentation -- its
background, issues, techniques, evolution, popular datasets, related work up to
the state of the art and future scope have been discussed. The paper provides
valuable information for those who want to do research in the field of instance
segmentation.
