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http://dx.doi.org/10.7848/ksgpc.2022.40.3.147

Guidelines for Data Construction when Estimating Traffic Volume based on Artificial Intelligence using Drone Images  

Han, Dongkwon (Dept. of Future Energy Engineering, Division of Environmental and Energy Engineering, Dong-A University)
Kim, Doopyo (Dept. of Civil Engineering, Dong-A University)
Kim, Sungbo (Dept. of Drone & Spatial Information Engineering, Youngsan University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.40, no.3, 2022 , pp. 147-157 More about this Journal
Abstract
Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.
Keywords
Drone Images; Artificial Intelligence; Object Recognition; Traffic; Deep Learning; Guidelines for Data Construction;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z., and Wang, Y. (2016), Real-time bidirectional traffic flow parameter estimation from aerial videos, IEEE Transaction of Intelligent Transportation System, Vol. 18, No. 4, pp. 890-901. https://doi.org/10.1109/TITS.2016.2595526.   DOI
2 Coifman, B. (2006), Vehicle level evaluation of loop detectors and the remote traffic microwave sensor, Journal of Transportation Engineering, Vol. 132, pp. 213-226. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:3(213).   DOI
3 Choi, I.K. and Yoo, J.S. (2017), Object detection in road environment CCTV images using deep learning. The Institute of Electronics and Information Engineers, 24-25 November, Incheon, Korea, pp. 627-629.
4 Girshick, R. (2015), Fast R-CNN, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7-13 December, Santiago, Chile, pp. 1440-1448. https://doi.org/10.1109/ICCV.2015.169.   DOI
5 Hamid, K.R., Talukder, A., and Islam, A.E. (2018), Implementation of fuzzy aided kalman filter for tracking a moving object in two-dimensional space, International Journal of Fuzzy Logic Intelligent Systems, Vol. 18, No. 2, pp. 85-96. https://doi.org/10.5391/IJFIS.2018.18.2.85.   DOI
6 He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2020), Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, pp. 386-397. https://doi.org/10.48550/arXiv.1703.06870.   DOI
7 Jung, Y.S. and Jung, D.Y. (2018), Class 1.3 vehicle classification using deep learning and thermal image, Journal of The Korean Institute of Intelligent Transportation Systems, Vol. 19, No. 6, pp. 96-106. (in Korean with English abstract) https://doi.org/10.12815/kits.2020.19.6.96.   DOI
8 Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014), Rich feature hierarchies for accurate object detection and semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23-28 June, Columbus, USA, pp. 580-587. https://doi.org/10.48550/arXiv.1311.2524.   DOI
9 Han, S.H., Shin, Y.S., and Lee, J.Y. (2019), A study on the evaluation technique of intelligent security technology based on spatial information : multi-CCTV collaboration technology, The Journal of the Korea Academia-Industrial cooperation Society, Vol. 20, No. 7, pp. 111-118. (in Korean with English abstract) https://doi.org/10.5762/KAIS.2019.20.7.111.   DOI
10 Kenney, J.B. (2011), Dedicated short-range communications (DSRC) standards in the United States, Proceeding of the IEEE, Vol. 99, No. 7, pp. 1162-1182. https://doi.org/10.1109/JPROC.2011.2132790.   DOI
11 Kim, Y.M., Lee, J.Y., Yoon, I.L., Han. T.J., and Kim, C.Y. (2018), CCTV object detection with background subtraction and convolutional neural network. The Korean Institute of Information Scientists and Engineers, Vol. 24, No. 3, pp. 151-156. (in Korean with English abstract) https://doi.org/10.5626/KTCP.2018.24.3.151.   DOI
12 Ren, S., He, K., Girshick, R., and Sun, J.(2015), Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149. https://doi.org/10.48550/arXiv.1506.01497.   DOI
13 Kim, J., Sung, J., and Park, S. (2020). Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition. IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), pp. 1-4. https://doi.org/10.1109/ICCE-Asia49877.2020.9277040.   DOI
14 Lee, T.H., Kim, K.J., Yun, K.S., Kim, K.J., and Choi, D.H. (2020), A method of counting vehicle and pedestrian using deep learning based on CCTV, Journal of Korean Institute of Intelligent Systems, Vol. 28, No. 3, pp. 219-224. (in Korean with English abstract) https://doi.org/10.5391/JKIIS.2018.28.3.219.   DOI
15 Park, H.L., Byun, S.H., and Lee, H.S. (2020), Application of deep learning method for real-time traffic analysis using UAV. Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 38, No. 4, pp. 353-361. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2020.38.4.353.   DOI
16 Seo, H.D. and Kim E.M. (2020), Estimation of traffic volume using deep learning in stereo CCTV image. Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 38, No. 3, pp. 269-279. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2020.38.3.269.   DOI
17 Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016), You only look once: unified, real-time object detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 June - 1 July, Las Vegas, USA, pp. 779-788. https://doi.org/10.48550/arXiv.1506.02640.   DOI
18 Seo, S.H. and Lee, S.B. (2018), A study on traffic data collection and analysis for uninterrupted flow using Drones, Journal of The Korean Institute of Intelligent Transportation Systems, Vol. 17, No. 6, pp. 144-152. (in Korean with English abstract) https://doi.org/10.12815/kits.2018.17.6.144.   DOI