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2025 | OriginalPaper | Chapter

A State-of-the-Art Review of Deep Learning-Based Object Detection Methods and Techniques

Authors : Chhaya Gupta, Nasib Singh Gill, Preeti Gulia

Published in: Innovative Computing and Communications

Publisher: Springer Nature Singapore

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Abstract

This chapter delves into the state-of-the-art methods and techniques used in deep learning-based object detection. It begins with an introduction to the challenges and evolution of object detection, emphasizing the superiority of deep learning over traditional methods. The study explores region proposal-based frameworks like R-CNN, Fast R-CNN, and Faster R-CNN, as well as classification-based frameworks such as SSD and YOLO. Additionally, the chapter discusses segmentation models like Mask R-CNN, which provide pixel-level object detection. The architecture of CNNs and prominent models such as AlexNet, VGGNet, ResNet, and DenseNet are also detailed, along with their applications in human and face detection, weapon detection, and pedestrian detection. The chapter concludes by highlighting the progress and future research directions in the field of object detection.

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Title: A State-of-the-Art Review of Deep Learning-Based Object Detection Methods and Techniques
Authors: Chhaya Gupta
Nasib Singh Gill
Preeti Gulia
Publisher: Springer Nature Singapore
Book: Innovative Computing and Communications
Print ISBN: 978-981-9741-51-9

Electronic ISBN: 978-981-9741-52-6

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-981-97-4152-6_35

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