Semantic Segmentation
Semantic segmentation is a pixel wise annotation, where every pixel in the image is assigned to a category. These classes could be pedestrian, car, bus, road, sidewalk, etc., and each pixel carries a semantic meaning. Semantic Segmentation is usually used in instances where environmental context is very important. For example, it is used in self-driving cars and robotics because it is for the models to understand the environment, they are operating in. Image annotation tools such as the popular Computer Vision Annotation Tool CVAT help to provide information about an image that can be used to train computer vision models.
Semantic Segmentation is the process of assigning a label to every pixel in the image. This is in stark contrast to classification, where a single label is assigned to the entire picture. Semantic segmentation treats multiple objects of the same class as a single entity. It describes the process of classifying each pixel of an image with a class label such as person, cars, truck, flowers, ocean etc.
- In order to execute a semantic segmentation a high level of understanding of the image is required, so that the algorithm can figure out the object present and also the pixels which have a similarity to the object.
- The process of labelling each pixel of an image with a similar class is commonly termed as dense prediction.
- In order to annotate images in semantic segmentation, annotators need to outline the object carefully using the pen tool.
- We need to make sure to touch another end to cover the object entirely that will be shaded with a specific color to differentiate the object from nearby other objects.
Semantic Segmentation is used in various real-life applications. Kindly please have a look at the examples mentioned below:
Self-Driving
In this use case people will see how semantic segmentation helps us in identifying lanes, vehicles, people and other objects of interest. The result is used to make intelligent decisions to guide the vehicle properly.
Medical Segmentation
In this use case people will find how semantic segmentation is used to identify important elements in medical scans. It is very useful to identify abnormalities such as tumors and other diseases instantly. This technique plays a vital role in our human lives cause due to this doctor are able to also identify the core of the problem of a patient. So, when this technique is used by the annotators, they always must keep in mind that a patient’s life is at stake if any kind of mishaps happen from the annotation team. This technique requires a high level of accuracy and a low recall for algorithms which are of high importance for these applications. This is also another way of automating less critical operations such as estimating the volume of organs from these semantically segmented scans.
Aerial Image Processing
Semantic Segmentation is used to identify types of land from aerial image view. The general use cases involve segmenting water bodies to provide accurate map information. It would also involve mapping roads, identifying types of crops, identifying free parking space and so on.
Conclusion
Semantic Segmentation algorithms have paved the way for greater adoption in real life applications and ML concept is enhancing in a more simplified way to enable annotators to execute the segmentations easily with the proper algorithms, and delivering high end knowledge to the Machine so that the work process reaches the highest realms of deep learning.
