APPROACHES OF BUILDING A REAL-WORLD OBJECT DETECTOR DATA SOURCE

Abstract

Object detection is a crucial task in computer vision and AI applications, enabling machines to identify and locate objects within images or video frames. The accuracy and performance of an object detector heavily rely on the quality and diversity of the training data. Several structured approaches of building an object detector data source have been build, drawing inspiration from Apple's Create ML documentation on the topic. Additionally, real-world applications available on both the App Store and Google Play that leverage object detection technology are showcased.