Make Object Detection and Identification Easier with Bounding Box Annotation

From self-driving cars to facial recognition technology, computer vision-based applications are the face of new-gen tech. Image processing is one of the major reasons that computer vision is improving continually and drives innovative AI-driven technologies. Do you think image processing is as simple as drawing rectangles around objects of interest? Take a look…

In spite of the rapid advances in AI and ML, matching the precise perception of humans is still a challenge for computer vision models. This is because, unlike humans, AI doesn’t have ages of evolution to help with data processing, thus creating the need for image annotation.

Annotated images enable the smart models to understand and interpret the input datasets. The tags and labels added to the training datasets help the machine learning algorithm to calculate attributes easily. Also, training data is as important as algorithms for the successful implementation of AI/ML.

The more accurate the training datasets, the more effective the model prediction. So, how to annotate data? There are many ways to go, but it all depends on your use case. Bounding box annotation is one such technique used to label images.

Industry Use Cases

The bounding boxes highlight a computer vision model’s training and testing data. Machine learning algorithms won’t be able to detect the objects of interest without these annotations—it is no wonder why they are fundamental for image annotation. Take a look at some of the common but interesting use cases:

1. eCommerce and Retail

Bounding boxes help with object detection and localization. Online shops and retail stores use this feature to ensure better product visualization. Visual perception models fed with this data can easily recognize objects such as pieces of furniture, fashion items, skincare products, etc. Apart from this, bounding box annotation also addresses other problems in the retail sector including chaotically organized supply chains, continuous digitization process, incorrect search results, and so on.

2. Autonomous Vehicles

Bounding box annotation helps the machine learning algorithms to detect different objects on the road such as traffic lights, other vehicles, street signs, lanes, potholes, pedestrians, etc. The more versatile and extensive the training data, the better AI-based machines can recognize objects on the streets and execute instructions according to the perceived information.

3. Insurance Claims

The insurance companies use bounding boxes to train a model that can quickly identify accidents and mishaps. Computer vision models can easily identify broken window glasses, damage on the body, roof, front, and tail light, as well as other defects. The machine learning algorithms can also estimate the level of damage, thus enabling insurance companies to process claims accordingly.

4. Agriculture

Identification of plant disease in early stages increases the chances of prevention in early stages. With the development of smart farming, there comes a unique challenge of collecting data to train AI/ML models to detect plant diseases and growth rates. The professionals use the bounding box annotation technique to give that necessary vision to machines.

5. Drone Imagery and Robotics

It is interesting to note that the applications of bounding boxes go beyond self-driving cars and retail products to object recognition with drone imagery and robotics. The unmanned aerial vehicles or drones can spot the migration of species, AC units, and damaged roofs when combined with accurately annotated training data. It becomes easier for drones and bots to detect physical objects present at far distances with the variety of elements annotated using a bounding box.

• Don’t forget to check out: Decoding the True Potential of Visual Data with Image Recognition Services

Final Thoughts

All in all, businesses across the industries are transforming as a result of AI/ML integration and further advances are only making its adoption inevitable. Image labeling techniques like semantic segmentation, polylines, bounding box annotation, etc., are what make computer vision a reality and take it to the future.

Consulting professionals to help you with AI/ML implementation and harness the true potential of your visual data is the smart move to cut through the competitive curve.