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http://dx.doi.org/10.7848/ksgpc.2020.38.6.573

Effect of the Learning Image Combinations and Weather Parameters in the PM Estimation from CCTV Images  

Won, Taeyeon (Dept. of Advanced Technology Fusion, Konkuk University)
Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University)
Sung, Hong ki (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
Chong, Kyu soo (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
Youn, Junhee (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.6, 2020 , pp. 573-581 More about this Journal
Abstract
Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.
Keywords
Deep Learning; PM Index; SVR (Support Vector Regression); CCTV; Convolutional Neural Network;
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