What is the difference between instance segmentation and object segmentation?

Difference Between Instance Segmentation and Object Segmentation

Object Segmentation: This technique, also known as semantic segmentation, involves dividing an image into segments based on the objects present in the image. This approach groups together all objects of the same class to one segment without differentiating individuals in instances where multiple objects of the same class are presented.
Instance Segmentation: Distinct from object segmentation, instance segmentation not only identifies the objects within an image but also differentiates between individual instances of each class. For example, in an image with multiple cars, each car is segregated from the other.

In Object Segeneration, each pixel is labeled with a class but not distinguished by instance. Hence, all pixels belonging to cars would be labeled the same regardless of how many cars are present.
Instance Segmentation, on the other hand, assigns a unique label to each instance of a particular object, thereby not only recognizing what the objects are but also how many are there in the scene.

In contexts where the focus is just on understanding the types of objects in the image without needing individual details, Object Segmentation is usually sufficient. This could include applications in landscape analysis, where knowing what elements are present (like trees, lakes) is enough without needing to differentiate between individual elements.
Instance Segmentation finds its application in scenarios where the differentiation between each object is crucial. Such instances come up in autonomous driving technology where each vehicle, pedestrian, and object needs to be distinctly identified and tracked throughout images.