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http://dx.doi.org/10.7839/ksfc.2021.18.4.009

Development of Virtual Simulator and Database for Deep Learning-based Object Detection  

Lee, JaeIn (Department of Mechanical Engineering, Sungkyunkwan University)
Gwak, Gisung (Department of Mechanical Engineering, Sungkyunkwan University)
Kim, KyongSu (Department of Mechanical Engineering, Sungkyunkwan University)
Kang, WonYul (Institute of Vehicle Engineering)
Shin, DaeYoung (Institute of Industrial Technology)
Hwang, Sung-Ho (Department of Mechanical Engineering, Sungkyunkwan University)
Publication Information
Journal of Drive and Control / v.18, no.4, 2021 , pp. 9-18 More about this Journal
Abstract
This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.
Keywords
Database; Object Detection; Image Segmentation; Deep Learning; Relational Database Management System;
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  • Reference
1 Y. Zhang et al., "Multiple Sclerosis Identification by Convolutional Neural Network with Dropout and Parametric ReLU", Journal of computational Science, Vol.28, pp.1-10, 2018.   DOI
2 F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions.", arXiv:1610.02357, 2016.
3 Y.-H. Im et al., "Real-Time Simulation of an Excavator Considering the Functional Valves of the MCV", Journal of Drive and Control, Vol.16, No.4, pp.38-47, 2019.   DOI
4 K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", The IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.
5 A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Communication of ACM, Vol.60, Issue 6, pp.84-90, 2017.   DOI
6 K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition", International Conference on Learning Representations, 2015.
7 H. J. Jang, et al., "A Study on the Construction of Deep Learning Dataset based on Virtual Lidar Sensor Point Cloud", Kookmin University, 2019.
8 S. J. Yoon et al., "Development of Autonomous Vehicle Learning Data Generation System", The Journal of The Korea Institute of Intelligent Transportation Systems, Vol.19 No.5 pp 162~177.
9 C. Yu, et al., "BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation", arXiv: 1808.00897, 2018.
10 L. C. Chen et al., "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs", arXiv:1606.00915, 2016.
11 MOCS: A Dataset and Benchmark for Detecting Moving Objects in Construction Sites
12 V. Badrinarayanan, A. Kendall, and R.Cipolla "Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.", arXiv:1511.00561, 2015.
13 Cityscapes Semantic Understanding of Urban Street Scenes, http://www.cityscapes-dataset.com/, 2021.
14 Pascal VOC Dataset Mirror, http://www.pjreddie.com/projects/pascal-voc-dataset-mirror/, 2021.
15 Python, https://www.python.org/, 2021.
16 D. Yu et al., "Hybrid Control Strategy for Autonomous Driving System using HD Map Information", Journal of Drive and Control, Vol.17, No.4, pp.80-86, 2020.   DOI
17 KITTI Sematic Segmentation Evaluation, http://www.cvlibs.net/datasets/kitti, 2021.