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http://dx.doi.org/10.7848/ksgpc.2022.40.1.15

Analysis of the Effect of Learned Image Scale and Season on Accuracy in Vehicle Detection by Mask R-CNN  

Choi, Jooyoung (Dept. of Environmental Health Science, Dept. of Technology Fusion Engineering, Konkuk University)
Won, Taeyeon (Dept. of Advanced Technology Fusion, Konkuk University)
Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.40, no.1, 2022 , pp. 15-22 More about this Journal
Abstract
In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.
Keywords
Deep Learning; Mask R-CNN; Remote Sensing; Object Detection;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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