Deep Learning for Analyzing the Data on Object Detection and Recognition

Abstract

The new era ushered in a steep rise in the domain of computer vision science. Computer vision faced a rudimentary problem of detection and recognition of objects in visual recognition. This has been thoroughly analyzed for the past nearly 10 years. The stated object detection and recognition is the process to classify as well as localize objects for detecting such objects again. The goal of Visual Object Detection (VOD) is to precisely find objects in such target classes with a position in the provided image, as well as to attach an appropriate class mark to each instance of the object. Due to the enormous achievements in the Deep Learning processing of images and detection of objects, Deep Learning object recognition approaches are recently required in an active way for a few years. The commonly used technologies for object detection and recognition are human-computer interaction, video monitoring, satellite imaging, transport system, and movement recognition. Considering the larger family of architectural setups of Deep Learning, a Convolutional Neural Network (CNN), composed of a series of layers within the network of neurons, is utilized for visual imagery. Architectural setups of CNNs of the Deep Learning type produce promising outcomes for the identification of digital image artifacts. Deep Learning, especially Deep CNNs, in recent times has received growing interest in object detection and recognition. Deep Learning stretched the feasible boundaries in the field of Digital Image Processing. This chapter provides a comprehensive overview of recently occurring advances in object detection and object recognition using CNNs. This examines the object detection’s characteristics and recognition models, the available benchmark datasets, and the research that took place on the implementation of object detection and recognition models for different applications.