Image annotation has helped computer vision to develop on a scale that it has never done over the years. It includes different techniques over different use cases. The process of annotating images can be as simple as drawing rectangles over objects in the images. But this process serves a greater purpose in later computer vision tasks. Bounding box is one such technique of image annotation.
Bounding Box Technique
A bounding box is a rectangular structure superimposed over an image including all important features of a particular object residing in it. However, It is one of the simplest and low time taking techniques of image annotation. The annotator outlines the objects of the images in a box as per the project requirements. Its purpose is to reduce the range of search for the object features and thereby conserve computing resources. It not only helps to classify the objects but also helps in object detection.
Object Classification and Object Detection
In object classification, the algorithm looks at an image and classifies the object in it. In fact, Convolutional neural network is the technique that makes this possible. Image recognition has a wide variety of applications ranging from face detection to cancer detection.
Object detection is the process of categorizing objects along with its location in the image. Generally, it is done by bounding boxes around the object in the image. The box contains information about the object and also coordinates which carries information of where it is located in the image. Object detection, therefore, is a combination of object classification and object localization. One of the main applications of object detection is self-driving cars. As it require to leverage ideas learned from both image classification and image localisations. CNN alone can fulfill object recognition. While object detection requires other additional techniques.
Bounding Box in Object Detection
The annotators outlines the objects in boxes as per the project requirements. When looking for a car the algorithm only searches in the bounding boxes labeled cars rather than looking for it in the whole image. The bounding box contains coordinates which has information about where exactly the object resides in the image.
The image shows the coordinates of the bounding box annotation. To find the car from this image, the algorithm tends the system to look only inside these coordinates instead of looking at the whole image for the car. Thereby easing the detection job of the model.
In short, just a single bounding box cannot enable a 100% prediction rate in the model. For this purpose, we need to feed the machines with a larger number of bounding boxes or simply “training data” for enhanced detection of objects in the image.
Object Detection Using Different Algorithms
Over the course of time, we have discovered different algorithms to perform the single task of object detection with more precision and accuracy. To illustrate some of the different types of algorithms are:
- R-CNN
- SPP
- Fast R-CNN.
- Faster R-CNN.
- Feature Pyramid networks.
- Yolo Framework — Yolo1, Yolo2, Yolo3.
- SSD
Moreover, Find here a detailed content on different types of algorithms used in object detection.
Use Cases
In brief, Let’s discuss different use cases implement bounding box technique like;
1) Autonomous cars
The bounding boxes are widely used in training the self-driving cars to recognize various objects that comes on the road like traffic signals, lanes, obstacles and pedestrians etc. All objects are annotated with bounding boxes in order to make it recognizable for machines. Hence this helps vehicles to understand its surroundings and to avoid crashes and navigate safely even through busy streets.
2) E-commerce
Used to train perception models for recognizing various fashion accessories and furniture.
3) Insurance
Identification of any damages or safety parameters from real time images to train machines that can detect degree of damage for any sort of claims.
4) Medical
In medical field bounding boxes are used to find the infected, broken areas and other defected areas to train the machines which work for its diagnosis and treatments etc.