NDLI: Combination of modified U-Net and domain adaptation for road detection

Content Provider	IET Digital Library
Author	Dong, Ming Zhao, Xiangmo Fan, Xing Shen, Chao Liu, Zhanwen
Abstract	Road detection is one of the crucial tasks for scene understanding in autonomous driving. Recently, methods based on deep learning had rapidly grown and addressed this task excellently, because they can extract more abundant features. In this study, the authors consider the visual road detection problem as a classification for each pixel of the given image, which is road or non-road. There is complex illumination encounter in traffic applications, so that the detection model has poor adaptability. They address this problem by proposing a deep network architecture, which combines the network U-Net-prior and domain adaptation model (DAM). U-Net-prior is a modified segmentation network which integrates location prior and shape prior into U-Net. DAM is a model for reducing the gap between training images and test images, which is optimised in adversarial learning to make the features extracted from different datasets close to each other. They validate the effectiveness of each component of the algorithm, and compare the overall architecture with other state-of-the-art methods, and the results show that the architecture achieves top accuracies with the shortest run time in monocular-vision-based methods, simultaneously, compared with the methods based on other sensors, the architecture also achieves a competitive result.
Starting Page	2735
Ending Page	2743
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6696
Journal	IET Image Processing
Publisher Date	2019-06-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adversarial Learning Autonomous Driving Complex Illumination Encounter Computer Vision Computer Vision And Image Processing Technique DAM Deep Learning Deep Network Architecture Detection Model Domain Adaptation Model Feature Extraction Image Classification Image Recognition Image Segmentation Knowledge Engineering Technique Learning in AI Modified Segmentation Network Modified U-Net Monocular-vision-based Method Network U-Net-prior Object Detection Road Vehicle Roads Scene Understanding Test Image Traffic Engineering Computing Training Image Visual Road Detection Problem
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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