Data annotation is a key part of AI to help machine learning models make accurate predictions by training the models with quality labeled data. While many types of data annotation are used in machine learning, image data annotation is one of the commonly used methods in training computer vision models.
Various ways to perform image data annotation depend on the use case, with bounding boxes being one of the most efficient methods.
Bounding box annotation uses bounding boxes or rectangular boxes to mark or define certain objects in the given image data. Bounding box annotation makes it easier for machine learning models to perform object detection and localization tasks more efficiently and accurately.1
This can be used to describe the location and size of an object in an image, but it also has other applications. For example, bounding boxes are often used when creating feature detectors for computer vision tasks like image classification or object detection.
For many companies, image data annotation outsourcing guarantees security, accuracy, and efficiency. Outsourcing data annotation projects allow clients to save time, and achieve new heights in productivity, flexibility, and scalability.
Bounding box annotation is a very crucial step in image data annotation. It helps improve the accuracy of image recognition and enhances the efficiency of object detection, classification, and segmentation processes in images.
Autonomous vehicles: Bounding boxes help detect objects like pedestrians, street signs, traffic lights, etc. that can further train the self-driving models to recognize barriers and make the algorithm accurate for driver’s safety.
eCommerce and Retail: Bounding box annotation is helpful in labeling different products and categories such as fashion accessories, cosmetics, etc. which further makes search results accurate.2
Robotics and Drone imagery: Bounding boxes can help detect objects from a distance, like migratory species, damaged roofs, or large pieces, to prevent collisions and accidents.
Damage detection for insurance claims: Vehicle damage, such as broken window glasses, damaged roof, and tail lights, can be easily detected by bounding boxes to help prevent accidents and determine a cost estimate for insurance claims.
Farming: Image data annotation is utilized in the farming sector to predict the state of crops, mainly their growth and cultivation time. This reduces manual labor significantly, giving farmers a more accurate reading without going out to the field.
Bounding box annotation is a great way to annotate your work, but sometimes it can be tricky to get right. Here are some best practices you can follow:
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