Acknowledgement
이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.
References
- Abdulla, W. (2018), Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow (v2.1) [Computer software]. GitHub. https://github.com/matterport/Mask_RCNN/releases
- Bouraya, S. and Belangour, A. (2021), "Deep Learning based Neck Models for Object Detection: A Review and a Benchmarking Study", International Journal of Advanced Computer Science and Applications, Vol.12, No.11, pp.161-167.
- Cheng, B., Girshick, R., Dollar, P., Berg, A. C., and Kirillov, A. (2021), "Boundary IoU: Improving Object-centric Image Segmentation Evaluation", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp.15334-15342).
- Dung, C. V. (2019), "Autonomous Concrete Crack Detection Using Deep Fully Convolutional Neural Network", Automation in Construction, Vol.99, pp.52-58.
- Everingham, M., Van Gool, L., Williams, C. K., Wi nn, J., and Zisserman, A. (2010), "The Pascal Visual Object Classes (voc) Challenge", International Journal of Computer Vision, Vol.88, pp. 303-338.
- Golding, V. P., Gharineiat, Z., Munawar, H. S., and Ullah, F. (2022), "Crack Detection in Concrete Structures Using Deep Learning", Sustainability, Vol.14, No.13, p.8117.
- He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017), "Mask r-cnn", In Proceedings of the IEEE International Conference on Computer Vision (pp. 2961-2969).
- He, K., Zhang, X., Ren, S., and Sun, J. (2016), "Deep Residual Learning for Image Recognition", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.770-778).
- Jung, S. Y., Lee, S. K., Park, C. I., Cho, S. Y., and Yu, J. H. (2019), A method for detecting concrete cracks using deep-learning and image processing. Journal of the Architectural Institute of Korea Structure & Construction, Vol.35, No.11, pp.163-170.
- KALIS (2022), Detailed Guidelines for Implementing Safety and Maintenance of Facilities (Safety Inspection and Diagnosis) Report, Korea.
- Kim, A. R., Kim, D., Byun, Y. S., and Lee, S. W. (2018), "Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering", Journal of the Korean Geotechnical Society, Vol.34, No.12, pp.145-154.
- Kim, A. R., Kim, D., Byun, Y. S., and Lee, S. W. (2018), "Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering", Journal of the Korean Geotechnical Society, Vol.34, No.12, pp.145-154.
- Kim, J., Jang, A., Park, M. J., and Ju, Y. K. (2021), "Comparison Analysis of Machine Learning for Concrete Crack Depths Prediction Using Thermal Image and Environmental Parameters", Journal of Korean Association for Spatial Structures, Vol.21, No.2, pp.99-110.
- LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998), "Gradient-based Learning Applied to Document Recognition", Proceedings of the IEEE, Vol.86, No.11, pp.2278-2324.
- Lin, T. Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017), "Feature Pyramid Networks for Object Detection", In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.2117-2125).
- NARS (2015), Basic Research for the Development of Comprehensive Fiscal Statistics System for Infrastructure Report, Korea.
- Ozgenel, C. F. (2018), "Concrete Crack Images for Classification", Mendeley Data, v1 http://dx.doi.org/10.17632/5y9wdsg2zt.1
- Park, J., Kim, K. Y., and Yun, T. S. (2023), "Morphological Analysis of Hydraulically Stimulated Fractures by Deep-Learning Segmentation Method", Journal of the Korean Geotechnical Society, Vol.39, No.8, pp.17-28.
- Simonyan, K. and Zisserman, A. (2014), Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.