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2020 | OriginalPaper | Chapter

Convolutional Neural Networks Backbones for Object Detection

Authors : Ayoub Benali Amjoud, Mustapha Amrouch

Published in: Image and Signal Processing

Publisher: Springer International Publishing

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Abstract

Detecting objects in images is an extremely important step in many image and video analysis applications. Object detection is considered as one of the main challenges in the field of computer vision, which focuses on identifying and locating objects of different classes in an image. In this paper, we aim to highlight the important role of deep learning and convolutional neural networks in particular in the object detection task. We analyze and focus on the various state-of-the-art convolutional neural networks serving as a backbone in object detection models. We test and evaluate them in the common datasets and benchmarks up-to-date. We Also outline the main features of each architecture. We demonstrate that the application of some convolutional neural network architectures has yielded very promising state-of-the-art results in image classification in the first place and then in the object detection task. The results have surpassed all the traditional methods, and in some cases, outperformed the human being’s performance.

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Title: Convolutional Neural Networks Backbones for Object Detection
Authors: Ayoub Benali Amjoud
Mustapha Amrouch
Publisher: Springer International Publishing
Book: Image and Signal Processing
Print ISBN: 978-3-030-51934-6

Electronic ISBN: 978-3-030-51935-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-51935-3_30

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