How To Label Up Your Data For Semantic Segmentation using Deep Learning?

Labeling Data can pose some challenges in Computer Vision Models. Why is it so challenging? Because there are many types of techniques used to train the procedures. To train these algorithms, we need AI Training Datasets. Image annotation becomes helpful as it annotates the images containing a particular object to make it recognizable for machines. Semantic Segmentation is one of the image annotation techniques. Hence, it provides the right picture to the Computer Vision. Semantic Segmentation requires pixel-level accuracy. Therefore, it is a labor-intensive technique of image annotation. 

Let us get to know more about Semantic Segmentation.

It is a step in the progression from coarse to inference. Image segmentation is a computer vision model in which we label specific regions of an image according to what's available. Semantic Segmentation is the process of assigning a label to every pixel available in the photo. A single label is assigned to the entire picture. The technique treats many objects of the same kind as a single identity.  

Why use Semantic Segmentation for Image annotation?

Categorizing and classifying the objects via computer vision includes three processes. The procedure goes with:

  1. Classification
  2. Object Detection
  3. Image Segmentation 

Why is classification so important? Image Classification assists in recognizing the objects and existing properties in a particular image. Then, object detection allows us to move one step ahead by finding the accurate position of that object. Object Detection is possible through bounding box annotation. 

Image Segmentation lets us recognize and understand the pixel-level view of an image. In this technique, each pixel in an image belongs to a single class. Why do we use semantic segmentation? The supreme purpose of using it is to build a computer vision-based application that requires top-notch accuracy. 

How to Label Images for Semantic Segmentation? 

There are multiple labeling tools that can be unreliable and inefficient. GTS helps you with choosing the right one. Our Annotators have enough skills and expertise to annotate the semantic segmentation images. 

We have to outline the object carefully with a pen tool for annotating the image. It is important to know that the object must get covered properly. This tool allows to draw freehand as well as straight lines, just like the polygon tool, and there is also the option to erase the inaccurate outlines.   

Several Points to keep in mind:

  1. Bordering up- It is vital to share borders between objects. When we draw a new entity, if we overlap the boundaries of an already existing object, the new border will be shared. This technique works really well when we are labeling objects from the backgrounds before. But, there are times when we want to first draw foreground and then draw an object behind without messing up the criteria. 
  2. Contrasting and Brightness of entities- In semantic segmentation, every object is to be shaded with a specific color. But, multiple times, objects in dark or nighttime images become so hard t differentiate clearly. But, Image Annotation adjusts the brightness and contrast level. 
  3. Zooming and Exporting images- It is so good that we can also use zooming and panning features to do the job accurately. And while zooming the objects you can also use panning to check the objects in each image, whether, the objects have been properly annotated or not. Though there is an option to save the labeled images when you have done the semantic segmentation annotation on an image, you can export the label data that can be used as training data while training a machine learning model. 

Approaches of Semantic Segmentation 

A basic semantic segmentation architecture can be thought of as an encoder and a decoder network- 

The encoder is a pre-trained classification network like ResNei followed by a decoder.

The decoder has to semantically project different features onto the pixel space to get a dense classification. 

There are major 3 approaches. Let us explore them. 

  1. Region-Based- he region-based methods generally follow the “segmentation using recognition” pipeline, which first extracts free-form regions from an image and describes them, followed by region-based classification. At test time, the region-based predictions are transformed to pixel predictions, usually by labeling a pixel according to the highest-scoring region that contains it.
  2. FCN Convolutional Network-based- The original Fully Convolutional Network (FCN) learns a mapping from pixels to pixels, without extracting the region proposals. The FCN network pipeline is an extension of the classical CNN. The main idea is to make the classical CNN take as input arbitrary-sized images. The restriction of CNNs to accept and produce labels only for specific sized inputs comes from the fully-connected layers which are fixed. Contrary to them, FCNs only have convolutional and pooling layers which give them the ability to make predictions on arbitrary-sized inputs.
  3. Weakly supervised- Most of the relevant methods in semantic segmentation rely on a large number of images with pixel-wise segmentation masks. However, manually annotating these masks is quite time-consuming, frustrating, and commercially expensive. Therefore, some weakly supervised methods have recently been proposed, which are dedicated to fulfilling the semantic segmentation by utilizing annotated bounding boxes.

We know that you are in search of Image Annotators. GTS not only helps you with high-quality collection of image, video, text, and audio, but also helps you with AI Data Annotation. GTS Experts are trained in Image Data Annotation. We facilitate you with decades of individual experience processing multiple data points. Our team provides several data labeling services that cater to our client's requirements. We can help you with semantic segmentation, 3D box annotation, landmark annotation, polyline annotation, and polygon annotation. GTS provides you with a 24 X 7 service and builds custom automation processes for clients. Try us now to enjoy forever! We are ready to help.  

Global Technology Solutions is an emerging but reliable and affordable data annotation company, providing a complete image annotation solution for object detection in AI and Machine Learning with high-quality training data for different types of model developments into different fields like healthcare, retail, agriculture, automotive and autonomous machines or robotics with best level of accuracy.


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