Semantic Segmentation of the Submerged Marine Debris in Undersea Images Using HRNet Model |
Kim, Daesun
(Ocean Policy Institute, Korea Institute of Ocean Science and Technology)
Kim, Jinsoo (Department of Spatial Information Engineering, Pukyong National University) Jang, Seonwoong (IREM Tech Inc.) Bak, Suho (Research Institute, IREM Tech Inc.) Gong, Shinwoo (Bukyeong Ocean Engineering and Consulting Inc.) Kwak, Jiwoo (AllBigDat Inc.) Bae, Jaegu (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) |
1 | Ronneberger, O., P. Fischer, and T. Brox, 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation, arXiv preprint arXiv:1505.04597. https://doi.org/10.48550/arXiv.1505.04597 DOI |
2 | Radeta, M., A. Zuniga, N.H. Motlagh, M. Liyanage, R. Freitas, M. Youssef, S. Tarkoma, H. Flores, and P. Nurmi, 2022. Deep Learning and the Oceans, Computer, 55(5): 39-50. https://doi.org/10.1109/MC.2022.3143087 DOI |
3 | Cheng, Z. and D. Fu, 2020. Remote Sensing Image Segmentation Method based on HRNET, Proc. of IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, Sep. 26-Oct. 28, pp. 6750-6753. https://doi.org/10.1109/IGARSS39084.2020.9324289 DOI |
4 | Chin, C., A. Neo, and S. See, 2022. Visual Marine Debris Detection using Yolov5s for Autonomous Underwater Vehicle, Proc. of 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS), Zhuhai, China, Jun. 26-28, pp. 22-24. https://doi.org/10.1109/ICIS54925.2022.9882484 DOI |
5 | Kim, H., M. Kim, and Y. Lee, 2022. Research Trend of the Remote Sensing Image Analysis Using Deep Learning, Korean Journal of Remote Sensing, 38(5-3): 819-834 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.5.3.2 DOI |
6 | Jeong, M., N. Kim, M. Park, and H. Yoon, 2021. The Characteristics of the Compositions and Spatial Distributions of Submerged Marine Debris in the East Sea, Journal of the Korean Society of Marine Environment & Safety, 27(2): 295-307 (in Korean with English abstract). https://doi.org/10.7837/kosomes.2021.27.2.295 DOI |
7 | Li, Y., Y. Chen, G. Liu, and L. Jiao, 2018. A Novel Deep Fully Convolutional Network for PolSAR Image Classification, Remote Sensing, 10(12): 1984. https://doi.org/10.3390/rs10121984 DOI |
8 | Yaqub, M., J. Feng, M.S. Zia, K. Arshid, K. Jia, Z.U Rehman, and A. Mehmood, 2020. State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images, Brain Sciences, 10(7): 427. https://doi.org/10.3390%2Fbrainsci10070427 DOI |
9 | Kuroda, M., K. Uchida, T. Tokaia, Y. Miyamoto, T. Mukai, K. Imai, K. Shimizu, M. Yagi, Y. Yamanaka, and T. Mituhashi, 2020. The current state of marine debris on the seafloor in offshore area around Japan, Marine Pollution Bulletin, 161(A): 111670. https://doi.org/10.1016/j.marpolbul.2020.111670 DOI |
10 | Seong, S. and J. Choi, 2021. Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates, Remote Sensing, 13(16): 3087. https://doi.org/10.3390/rs13163087 DOI |
11 | KOEM (Korea Marine Environment Management Corporation), 2019. A study on the establishment of the 3rd Master Plan for Marine Debris Management, Korea Marine Environment Management Corporation, Seoul, Korea. |
12 | Kandel, I. and M. Castelli, 2020. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset, ICT Express, 6(4): 312-315. https://doi.org/10.1016/j.icte.2020.04.010 DOI |
13 | Kim, S., Y. Yang, J. Jeong, M. Song, D. Lee, M. Choi, S. Cha, C. Lee, and H. Kim, 2016. Verification on the Ghost Fishing by Derelict Fishing Gears through the Tank Experiments, The Korean Society for Fisheries and Marine Sciences Education, 28(5): 1258-1265 (in Korean with English abstract). https://doi.org/10.13000/JFMSE.2016.28.5.1258 DOI |
14 | Kingma, D.P. and J. Ba, 2015. Adam: A Method for Stochastic Optimization, Proc. of 3rd International Conference for Learning Representations, San Diego, CA, USA, May 7-9, pp. 1-15. https://doi.org/10.48550/arXiv.1412.6980 DOI |
15 | Politikos, D.V., E. Fakiris, A. Davvetas, I.A. Klampanos, and G. Papatheodorou, 2021. Automatic detection of seafloor marine litter using towed camera images and deep learning, Marine Pollution Bulletin, 164: 111974. https://doi.org/10.1016/j.marpolbul.2021.111974 DOI |
16 | Ruder, S., 2017. An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747. https://doi.org/10.48550/arXiv.1609.04747 DOI |
17 | Shinde, P., 2021. Marine Debris Segmentation using Capsule Network (SegCaps), https://norma.ncirl.ie/id/eprint/5220, Accessed on Dec. 14, 2021 |
18 | Xue, B., B. Huang, W. Wei, G. Chen, H. Li, N. Zhao, and H. Zhang, 2021b. An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 12348-12360. https://doi.org/10.1109/JSTARS.2021.3130238 DOI |
19 | Wang, T., S. Oh, H. Lee, D. Choi, J. Jang, and M. Kim, 2022. A Study on the Implementation of Real-Time Marine Deposited Waste Detection AI System and Performance Improvement Method by Data Screening and Class Segmentation 7, The Journal of the Convergence on Culture Technology, 8(3): 571-580 (in Korean with English abstract). http://dx.doi.org/10.17703/JCCT.2022.8.3.571 DOI |
20 | Wu, H., C. Liang, M. Liu, and Z. Wen, 2021. Optimized HRNet for image semantic segmentation, Expert Systems with Applications, 174: 114532. https://doi.org/10.1016/j.eswa.2020.114532 DOI |
21 | Yang, J. and G. Yang, 2018. Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer, Algorithms, 11(3): 28. https://doi.org/10.3390/a11030028 DOI |
22 | Xue, B., B. Huang, G. Chen, H. Li, and W. Wei, 2021a. Deep-Sea Debris Identification Using Deep Convolutional Neural Networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 8909-8921. https://doi.org/10.1109/JSTARS.2021.3107853 DOI |
23 | Song, J., Y. Lee, Y. Han, and Y. Park, 2022. Management status and policy direction of submerged marine debris for improvement of port environment in Korea, Open Geosciences, 14(1): 443-552. https://doi.org/ 10.1515/geo-2022-0368 DOI |
24 | Zhang, J., S. Lin, L. Ding, and L. Bruzzone, 2020. Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images, Remote Sensing, 12(4): 701. https://doi.org/10.3390/rs12040701 DOI |
25 | Zhou, X., 2021. An Improved Semantic Segmentation Model for Remote Sensing Images based on HRNet, Proc. of 2021 International Conference on Computer, Remote Sensing and Aerospace (CRSA 2021), Tokyo, Japan, Jul. 23-25, vol. 2006, pp. 1-6. https://doi.org/10.1088/1742-6596/2006/1/012046 DOI |
26 | Wang, J., K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang, W. Liu, and B. Xiao, 2019. Deep high-resolution representation learning for visual recognition, arXiv preprint arXiv:198.07919. https://doi.org/10.48550/arXiv.1908.07919 DOI |