NDLI: Deep learning-based vehicle detection with synthetic image data

Content Provider	IET Digital Library
Author	Wang, Ye Deng, Weiwen Liu, Zhenyi Wang, Jinsong
Abstract	Deep convolutional neural network (CNN)-based object detectors perform better than other kinds of object detectors in recent development. Training CNNs needs large amounts of annotated data. The acquisition and annotation of real images are arduous and often lead to inaccuracy. In order to solve this problem, the authors try to use synthetic images as substitute to train a vehicle detector. Annotation on synthetic images can be performed automatically, and can obtain better uniformity. Furthermore, it is also easy to get more variations in synthetic images. The authors present a pipeline to generate synthetic images for training vehicle detector. In this pipeline, many factors are considered to add more variations and extend the domain of training dataset. Thus, the detector trained with synthetic images is expected to perform well. The extent to which these factors influence detection performance is illustrated. However, the performance of a vehicle detector trained with synthetic images is not as good as that with real images because of domain gap. In this study, the authors develop a transfer learning approach to improve the performance of authors' vehicle detector with only a few manually annotated real images.
Starting Page	1097
Ending Page	1105
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 7, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5365
Journal	IET Intelligent Transport Systems
Publisher Date	2019-03-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Annotated Training Data Authors Computer Games Computer Vision And Image Processing Technique Convolutional Neural Network-based Object Detectors Deep Learning-based Vehicle Detection Image Classification Image Recognition Imaging Factors Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Object Detection Optical, Image And Video Signal Processing Synthetic Image Synthetic Image Annotation Synthetic Image Data Traffic Engineering Computing Training Vehicle Detector
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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