Deep learning-based image recognition for autonomous driving

Various image recognition tasks were handled in the image recognition field prior to 2010 by combining image local features manually designed by researchers (called handcrafted features) and machine learning method. After entering the 2010, However, many image recognition methods that use deep learn...

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Veröffentlicht in:IATSS research Jg. 43; H. 4; S. 244 - 252
Hauptverfasser: Fujiyoshi, Hironobu, Hirakawa, Tsubasa, Yamashita, Takayoshi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2019
Elsevier
Schlagworte:
Computer vision
Convolutional neural network
Deep learning
Image recognition
Deep learning
Computer vision
Image recognition
Convolutional neural network
ISSN:0386-1112, 2210-4240
Online-Zugang:Volltext
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Zusammenfassung:Various image recognition tasks were handled in the image recognition field prior to 2010 by combining image local features manually designed by researchers (called handcrafted features) and machine learning method. After entering the 2010, However, many image recognition methods that use deep learning have been proposed. The image recognition methods using deep learning are far superior to the methods used prior to the appearance of deep learning in general object recognition competitions. Hence, this paper will explain how deep learning is applied to the field of image recognition, and will also explain the latest trends of deep learning-based autonomous driving. •Convolutional neural networks for object detection and semantic segmentation•Visual explanation of making decisions for autonomous driving by end-to-end learning•End-to-end learning for autonomous driving
ISSN:0386-1112
2210-4240
DOI:10.1016/j.iatssr.2019.11.008