Object Detection Frameworks and Services in Computer Vision

Abstract

Object detection in image processing and computer vision not only classifies objects but also locates them in the image. It is used to detect semantic objects belonging to a specific class, such as animals, humans, cars, or buildings, inside an image or video frame. Classification models aid in predicting the class of distinct items in a given image. Object detection frameworks use confidence scores to classify objects in a given image and forecast their location. Object detection is thus the approach used to pinpoint various objects by drawing bounding boxes around them and specifying the predicted class. Object detection is a focused field of research that is seeing constant progress in model performance and accuracy due to significant developments in image processing and computer vision. Object detection models must classify and localize objects in images. The most challenging task in computer vision is calculating coordinates for building bounding boxes. This chapter provides a gentle introduction to image classification, localization, and object recognition using deep learning frameworks. It also includes a detailed overview of the evolution of object detection utilizing popular object detection models like the Region-based CNN family and YOLO (you only look once). It also covers the major application areas of object detection that use deep neural networks.