Image Annotation Service for Autonomous Vehicles

Image Annotation refers to the process of labelling an image automatically with the help of text label or labels. Image Annotation of photographs is very important to aid humans or computers to identify and recognize the different objects properly. Image Annotationis generally done with the help of human expert annotators, using pre-defined keywords. In short, Image Annotation of photographs is about adding metadata to an image, which helps machines to identify the same given objects in the digital image. Image classifiers are now available in many different platforms, which helps to perform this task.

A very important aspect is that these artificial intelligence (ANNs) models should be trained on large databases, prior to the usage in applications. This will help the system to understand each text label and its meaning. Image classifiers can also be used for pre-trained classifiers, which make use of the same labelled images. Image classifiers are now widely used for pre-trained visual classifications, such as face recognition and other image classification tasks.

Image classifiers based on deep learning technology can also be used for annotating images. Deep learning refers to the combination of convolutional and backpropagation schemes in order to achieve high levels of accuracy in labelled images. The artificial intelligence should allow the network to label images with similar classifiers. Deep learning makes use of a deep embedding scheme, so that the same labelled object is passed down the network, from one layer to the other, without having to store the whole image in memory.

Another major advantage of using artificial intelligence for Data Annotation is the speed. With the help of the Image Database, the training of the network to identify labelled images can be completed within hours. Moreover, the generated classifier can be trained over again using batch training or multiple batch processing. This improves the quality of the results, as the classifier improves with time. This also ensures that the output of the computer vision application is as accurate as possible.

Image classification using semantic segmentation is another good example of using artificial intelligence to classify labelled images. In this case, a strong artificial intelligence network is used to apply different annotators for every image that is fed into the software. The first group of labelled images is labelled with semantic segmentations. This method involves the use of words for tagging images such as labels such as “chairs”, “apparatus” etc. On the other hand, the “others” labelled images are also classified according to the human labels such as “man”, “woman” etc. In addition, the final set of all the labelled images is labelled with a unique key.

Image classification using the above two methods can dramatically cut down the learning curve for new users. This will in turn help them to rapidly accelerate their progress by skipping some steps. However, the biggest challenge remains in the adoption of these technologies in autonomous vehicles.

It is here that the internet, and specifically the Amazon’s Mechanical Pen, can play a significant role. This is because the use of Mechanical Pen will allow the development of applications that are able to classify images even without the supervision of an expert architect. Thus, the development of such autonomous vehicle applications will become more viable in the future.

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