@article{ART002639366},
author={sohn jung mo and Do-Soo Kim and Hye-Bin Hwang},
title={Improvement of learning concrete crack detection model by weighted loss function},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2020},
volume={25},
number={10},
pages={15-22},
doi={10.9708/jksci.2020.25.10.015}
TY - JOUR
AU - sohn jung mo
AU - Do-Soo Kim
AU - Hye-Bin Hwang
TI - Improvement of learning concrete crack detection model by weighted loss function
JO - Journal of The Korea Society of Computer and Information
PY - 2020
VL - 25
IS - 10
PB - The Korean Society Of Computer And Information
SP - 15
EP - 22
SN - 1598-849X
AB - In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.
KW - Deep Learning;Concrete Crack;Crack Detection;Loss Function;U-Net
DO - 10.9708/jksci.2020.25.10.015
ER -
sohn jung mo, Do-Soo Kim and Hye-Bin Hwang. (2020). Improvement of learning concrete crack detection model by weighted loss function. Journal of The Korea Society of Computer and Information, 25(10), 15-22.
sohn jung mo, Do-Soo Kim and Hye-Bin Hwang. 2020, "Improvement of learning concrete crack detection model by weighted loss function", Journal of The Korea Society of Computer and Information, vol.25, no.10 pp.15-22. Available from: doi:10.9708/jksci.2020.25.10.015
sohn jung mo, Do-Soo Kim, Hye-Bin Hwang "Improvement of learning concrete crack detection model by weighted loss function" Journal of The Korea Society of Computer and Information 25.10 pp.15-22 (2020) : 15.
sohn jung mo, Do-Soo Kim, Hye-Bin Hwang. Improvement of learning concrete crack detection model by weighted loss function. 2020; 25(10), 15-22. Available from: doi:10.9708/jksci.2020.25.10.015
sohn jung mo, Do-Soo Kim and Hye-Bin Hwang. "Improvement of learning concrete crack detection model by weighted loss function" Journal of The Korea Society of Computer and Information 25, no.10 (2020) : 15-22.doi: 10.9708/jksci.2020.25.10.015
sohn jung mo; Do-Soo Kim; Hye-Bin Hwang. Improvement of learning concrete crack detection model by weighted loss function. Journal of The Korea Society of Computer and Information, 25(10), 15-22. doi: 10.9708/jksci.2020.25.10.015
sohn jung mo; Do-Soo Kim; Hye-Bin Hwang. Improvement of learning concrete crack detection model by weighted loss function. Journal of The Korea Society of Computer and Information. 2020; 25(10) 15-22. doi: 10.9708/jksci.2020.25.10.015
sohn jung mo, Do-Soo Kim, Hye-Bin Hwang. Improvement of learning concrete crack detection model by weighted loss function. 2020; 25(10), 15-22. Available from: doi:10.9708/jksci.2020.25.10.015
sohn jung mo, Do-Soo Kim and Hye-Bin Hwang. "Improvement of learning concrete crack detection model by weighted loss function" Journal of The Korea Society of Computer and Information 25, no.10 (2020) : 15-22.doi: 10.9708/jksci.2020.25.10.015
