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[R] Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Abstract – Object detection, the computer vision task dealing with detecting instances of objects of a certain class (e.g., ’car’, ’plane’, etc.) in images, attracted a lot of attention from the community during the last six years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNNs). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way. This study covers not only the design decisions made in modern deep (CNN) object detectors, but also provides an in-depth perspective on the set of challenges currently faced by the computer vision community, as well as some complementary and new directions on how to overcome them. In its last part it goes on to show how object detection can be extended to other modalities and conducted under different constraints. This survey also reviews in its appendix the public datasets and associated state-of-the-art algorithms.

Page -> https://arxiv.org/abs/1809.03193

PDF -> https://arxiv.org/pdf/1809.03193.pdf

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.