Should the computer vision industry continue using bounding box annotations?

Author: Vahan Petrosyan, CTO at SuperAnnotate

In this post, I will share some ideas related to image annotation that I accumulated during my PhD research. Specifically, I will discuss the current state-of-the-art annotation methods, their trends, and future directions. Finally, I will briefly talk about the annotation software we are building and give a little preview about our company — SuperAnnotate.

Outline

Introduction to Image Annotation

Mainstream Annotation Methods: Bounding Box

Pixel-Precision in Image Annotation

About SuperAnnotate

1. Introduction to Image Annotation

Image annotation is the process of selecting objects in images and labeling them by their names. This is the backbone of the AI computer vision, where, for example, in order for your self-driving car software to accurately identify any object in the image, say a pedestrian, one needs hundreds of thousands to millions of annotated pedestrians. Other use cases include drone/satellite footage analytics, security and surveillance, medical imaging, e-commerce, online image/video analytics, AR/VR, etc.

The increase in image data and computer vision applications requires a huge amount of training data. Data preparation and engineering tasks represent over 80% of the time consumed in AI and Machine Learning projects. Therefore over the last few years, many data annotation services and tools have been created to cover the needs of this market. As a result, the data labeling became $1.5B market in 2018 and is expected to grow to $5B by 2023.

2. Mainstream Annotation Methods: Bounding Box

The most common annotation technique is the bounding box, which is the process of fitting a tight rectangle around the target object. This is the most used annotation approach since bounding boxes are relatively straight forward and many object detection algorithms were developed with this method in mind (YOLO, Faster R-CNN, etc). Therefore, all annotation companies offer solutions for bounding box annotation (services or software). However, box annotation suffers from major drawbacks: