Why pixel accuracy is the future of the image annotation for Machine Learning?
Training of drones, autonomous vehicles, and other computer-vision based models needs annotated images and videos so that the machines can identify and interpret the object without much human intervention. The data which is fed in these machine algorithms to understand images and videos, text or audio created the need for the annotations.
Majorly, image and video annotation are widely used. However, the process of annotating is almost the same but video annotation needs more precision and accuracy and it is a bit difficult because of the movement of the target object i.e. the target object continuously moves in a video so it is slightly difficult to annotate the videos as it needs specialization and experience.
Image annotation is one of the basic tasks to train the machines or computers to interpret and identify the visual world. Images annotated by the annotators are used to train machine learning algorithms which helps them to identify the objects that are given in the image. This gives computers the ability to see and identify the things as humans do.
Image annotation means selecting the given objects in the image and labelling them by their names. It helps machines to recognize things/objects so that they can make correct decision without any human intervention. For example, if a cat needs to be annotated then, that cat in the image will be marked and labelled as a cat and this data is fed into an algorithm to train the machine so that next time the machines can automatically recognize the object.
Pixel accurate image annotations
Based on algorithms there are several types of annotations. Few are:
- Bounding box annotation
- Polygon annotation
- Semantic annotation
- Key point annotation
- 3D point cloud annotation
- Landmark annotation
The most commonly used image annotation is the bounding box in which rectangle boxed are placed or marked around the target object. However, this has some major issues:
1. One needs a huge number of bounding boxes to reach over 95% detection accuracies.
2. This technique does not allow perfect detection regardless of how much data you use.
3. The detection becomes extremely complicated for obstructive objects.
Also Read: Top Data Labeling Challenges Faced by the Data Annotation Companies
All these issues which are mentioned above can be solved with a pixel-accurate annotation. For example, pixel level accuracy is of utmost importance is the medical field where machine learning models requires high level of precision and accuracy for the model to make sound judgment and deliver accurate results. Machine Learning Projects in Medical space are highly sensitive and depends significantly upon accuracy of the data being fed into them. Even minor inaccuracies in the medical machine learning data could be detrimental for the entire operations and could lead to disastrous results. Hence, this is where pixel-accurate annotations plays a huge part in keeping it together. And a lot of it depends upon the quality of the images and datasets.
Yet, the most commonly used tools are majorly dependent on point-by-point object selection, which is time-consuming and costly too. Pixel-accurate annotations have a huge advantage to aerial imagery as well. However, the tools for such annotations depend on the slow point-by-point annotation. As a result the time taken to complete the task is way too much and the results are also sensitive to human errors. To train an algorithm to identify the roof types in the satellite images, annotator needs to annotate thousands to millions of images of roofs in different cities, weather conditions, etc and when the image is not accurate and gets there timely then the technology and the output will suffer because the quality of image plays a crucial role in the annotation.
Also Read: A Complete Image Annotation Solution for Object Detection in AI and Machine Learning
However, there are researches that have helped in reducing the impact of image quality. Addressing this problem, the research community have made efforts towards creating more efficient pixel-accurate annotation methods. The community is developing many exciting pre-processing algorithms that we can use to improve image quality and ensure better quality segmentation.
A company whose competitive advantage depends on accurate image annotation can reach Anolytics as we are delivering best-in-class image annotation services with several others. The professionals in Anolytics have several years of technical experience in using machine learning and artificial intelligence technologies to develop projects in healthcare, retail, autonomous flying, self-driving, agriculture, robotics and among others. Here one will get the utmost satisfaction to meet your requirements at affordable pricing.
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