Instance Segmentation Vs. Object Detection: 3 Things You Need To Know

Instance Segmentation Vs. Object Detection
Instance Segmentation Vs. Object Detection

Discerning visual data needs a bunch of techniques in action.

Just like human vision enables us to understand the visual world, computer vision trains machines to see the world. Machines rely on techniques that help them draw meaning from visual data to understand what they see.

The most common yet highly applicable and effective underlying techniques computer vision tasks employ are object detection and instance segmentation.

These techniques help computer vision applications extract actionable information from images and use that data for automated tasks in image processing and AI object detection solutions.

Though both techniques are interrelated, they have a subtle difference between them and the functions they perform.

Our goal in this article is to show you how these techniques differ, in case you confuse them as one, and find out how each works.

So, let’s jump right in.

Explaining Instance Segmentation and Object Detection

Instance segmentation identifies instances in an image. It is a complex form of image segmentation that helps separate an example from its belonging class.

For example, the instance segmentation application that detects people will separate individuals and apply a different color on each to show them as distinct instances.

We find this technique particularly useful in complex visual environments where many objects of the same class are present and need to be distinguished.

Object detection is a part of the instance segmentation process. It helps create bounding boxes (vectors with four elements like w, x, y, and z) around each object in an image.

Within these boundaries, instance segmentation then identifies object instances.

Both techniques seek to replicate the human ability to comprehend visuals by leveraging AI, machine learning, and deep learning, which help them generate meaningful results.

Instance Segmentation Vs. Object Detection: 3 things you need to know

1. Why Instance Segmentation and Object Detection Matter

  • Instance Segmentation

Instance segmentation has numerous uses in domains like medical and satellite imagery.

In a histopathologic image which usually contains a large number of nuclei of different shapes around the cytoplasm, instance segmentation detects and separates the nucleus for granular processing. This processing helps detect diseases such as cancer.

Similarly, satellite imagery often contains objects of tiny sizes which are complex and intricate to separate due to their close placement.

Instance segmentation uses a network architecture to achieve better results from satellite images. It also plays an important part in sea pollution monitoring and ship detection in maritime security.

  • Object Detection

Advanced driver assistance systems in automated cars rely on object detection to detect pedestrians and navigate through driving lanes. Object detection is also the technology behind surveillance and image retrieval systems.

The application of object detection techniques helps improve safety in driving and the security of premises.

2. How Instance Segmentation and Object Detection Work

  • Instance Segmentation

Instance segmentation looks for lookalike objects and identifies each object from the same class as an individual instance.

To train the model for this segmentation, we need to store the description of that instance in a database.

At runtime, the system would use a “matching score and threshold” to see whether that instance is present or not.

  • Object Detection

Object detection uses a variety of techniques. For object detection through deep learning, there are two key approaches:

  • Use a pre-trained object detector

Object detection performed with deep learning uses transfer learning. This approach enables you to work with a pre-trained model and fine-tune it for your application. With this method, you get faster results since the model is pre-trained on millions of images.

  • Create and train a custom object detector

Training a custom object detector involves work from scratch. You need to develop a network architecture that learns the features of objects and compile a large dataset of labeled images for training CNN. A custom system can give you remarkable results. However, you need to manually add layers and weights in CNN by putting some extra time and effort.

3. Difference Between Instance Segmentation Vs. Object Detection

Think of instance segmentation as a process that combines semantic segmentation and object detection.

Whereas object detection coarsely spots multiple objects in images by drawing boundaries around them and semantic segmentation creates pixel-level categories for object classes, instance segmentation generates a segment map for each type. It localizes instances of each object from all possible classes.

As compared to object detection and semantic segmentation, instance segmentation produces more meaningful inference of images and gives richer output.

So, the difference between instance segmentation and object detection techniques is that object detectors only detect objects in images. Conversely, instance segmentation solutions provide a fine-grained understanding of image data by defining and classifying each instance present in visual input.

Conclusion

Object detection and instance segmentation are a few rapidly growing computer vision techniques.

Since they find large-scale applications in a wide range of scenarios, their evolution can provide us with more granular level data, resulting in better conclusions.

If additional research and experiments occur in these areas, these methods can enable machines to perform much more complex tasks.

This way, in the future, we will be able to prepare more machines to take on various challenging tasks in more complex scenarios.

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