A study of the landforms and megafaunal characteristics of the Caiwei Guyot area by manned submersible image data-driven technology
-
Abstract: Scientific and precise evaluations of the megafaunal and landform characteristics of seamounts are important guides for their protection and study. A series of manned and unmanned submersibles have provided invaluable observational imaging data for the ecological study of seamounts. However, traditional methods of artificial observation of seamount imaging data cannot accurately and efficiently determine the characteristics of megafauna and landforms. This research harnesses data-driven technology to systematically investigate the distributional traits and morphological features of megafaunal organisms, as well as the topographical characteristics, in the Caiwei Guyot region of the western Pacific's Magellan Seamounts. To construct the landform and megafauna dataset of the Caiwei Guyot region, we used a data preprocessing technology based on image enhancement to provide high-quality imaging data for data-driven technologies. A megafaunal identification and counting algorithm based on YOLOv5 (You Only Look Once Version 5) was developed to efficiently assess the abundance, variety, and dominant species of megafauna. Simultaneously, a landform three-dimensional (3D) reconstruction algorithm based on PatchmatchNet was developed to reconstruct the 3D form of the terrain accurately. This study pioneers the application of data-driven technology to deep-sea imaging within the Caiwei Guyot region, offering an innovative approach to accurately and efficiently characterize the region's unique megafauna and landforms.
-
Key words:
- manned submersible imaging /
- data-driven /
- Caiwei Guyot /
- landforms /
- megafauna
-
Table 1. Video parameters
Resolution ratio Frame rate format Encoding method Color space 1920 × 1080 25 H.264 RGB Table 2. Comparison of experimental results of the UIQM index
Image Original MSRCR DCP AGCW This method 1 0.74 2.34 0.93 1.09 4.50 2 2.03 2.99 2.43 1.82 4.89 3 1.53 2.48 2.07 1.55 4.99 4 2.87 3.28 4.02 3.05 4.87 Average 1.80 2.77 2.36 1.88 4.81 Table 3. Comparative experimental results of different algorithms
Model mAP0.5 (%) mAP0. 5:0.95 (%) Param (M) Faster RCNN 67.9 44.4 137.1 YOLOv3 94.9 79.1 61.5 YOLOv3-Tiny 93.9 74.5 8.7 YOLOv7-Tiny 91.1 67.6 6.0 YOLOv5n 93.6 72.9 1.8 Table 4. Species composition
Phylum Class Order/family/genus Number Porifera Demospongiae Callyspongiidae sp. 41 Cladorhizidae sp. 3 Chondrocladia sp. 3 Hexactinellida Caulophacus sp. 16 Poliopogon sp. 19 Hyalonema sp. 12 Corbitellinae sp. 17 Bolosoma sp. 5 Rhizophyta sp. 4 Tretopleura sp. 3 Saccocalyx sp. 1 Semperella sp. 106 Cnidaria Anthozoa Chrysogorgia sp. 5 Fungiacyathus stephanus sp. 1 Primnoidae sp. 14 Actinoscyphia sp. 1 Keratoisididae sp. 56 Iridogorgia sp. 6 Bathypathes sp. 3 Actinostolidae sp. 3 Actiniaria sp. 5 Annelida Polychaeta Phyllodocida sp. 1 Polynoidae sp. 2 Arthropoda Malacostraca Heterocarpus sp. 17 Aristeidae sp. 18 Nematocarcinus sp. 4 Echinodermata Crinoidea Crinoidea sp. 9 Hyocrinidae sp. 3 Bathycrinidae sp. 4 Pentametrocrinidae sp. 7 Asteroidea Freyastera sp. 7 Pterasteridae sp. 1 Asteroidea sp. 1 Freyellidae sp. 8 Freyastera basketa 5 Brisingidae sp. 2 Freyastera mortenseni 1 Echinoidea Echinoidea sp. 8 Holothuroidea Paroriza sp. 1 Hansenothuria sp. 2 Paelopatides sp. 32 Synallactidae sp. 13 Psychropotes sp. 11 Elpidiidae sp. 5 Enypniastes sp. 2 Molpadidemas sp. 4 Holothuroidea sp. 1 Peniagone sp. 1 Benthodytes sp. 3 Chordata Actinopterygii Aldrovandia afinis 10 Ophidiiformes sp. 9 Synaphobranchidae sp. 13 Halosauridae sp. 9 Ophidiidae sp. 15 Macrouridae sp. 6 Abyssoberyx sp. 3 Table 5. Main groups and dominance
Class Degree of dominance Demospongiae 0.005 Hexactinellida 0.05 Anthozoa 0.03 Polychaeta 0.0043 Malacostraca 0.0082 Crinoidea 0.0064 Asteroidea 0.007 Echinoidea 0.002 Holothuroidea 0.008 Actinopterygii 0.01 -
Aanæs H, Jensen R R, Vogiatzis G, et al. 2016. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120(2): 153–168, doi: 10.1007/s11263-016-0902-9 Ancuti C O, Ancuti C, De Vleeschouwer C, et al. 2018. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1): 379–393, doi: 10.1109/TIP.2017.2759252 Baker M R, Williams K, Greene H G, et al. 2021. Use of manned submersible and autonomous stereo-camera array to assess forage fish and associated subtidal habitat. Fisheries Research, 243: 106067, doi: 10.1016/j.fishres.2021.106067 Barnes C, Shechtman E, Finkelstein A, et al. 2009. PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics (TOG), 28(3): 24 Catmull E E. 1974. A subdivision algorithm for computer display of curved surfaces. Springfield: NTIS Chen Jiazhang, Meng Shunlong, You Yang, et al. 2009. Characteristic of phytoplankton community in Lake Wuli, Lake Taihu. Ecology and Environmental Sciences (in Chinese), 18(4): 1358–1367, doi: 10.16258/j.cnki.1674-5906(2009)04-1358-10 Collot J Y, Lallemand S, Pelletier B, et al. 1992. Geology of the d'Entrecasteaux-New Hebrides arc collision zone: results from a deep submersible survey. Tectonophysics, 212(3–4): 213–217, 221–241 Dai Jifeng, Qi Haozhi, Xiong Yuwen, et al. 2017. Deformable convolutional networks. In: Proceedings of 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 764–773 Ding Zhongjun. 2021. Operational Techniques for Deep-Sea Manned Submersible Exploration (in Chinese). Harbin: Harbin Engineering University Press, 26–35 Ding Zhongjun, Liu Chen, Li Dewei, et al. 2023. Deep-sea biological detection method based on lightweight YOLOv5n. Sensors, 23(20): 8600, doi: 10.3390/s23208600 Duan Xiaoxu, Duan Jun, Zhang Xiangxiang. 2023. Study on the typical micro-geomorphologic characteristics of seabed in a certain sea area in southern Zhuhai. Guizhou Science (in Chinese), 41(3): 59–62,91 Geyer R A. 1977. Submersibles and their use in oceanography and ocean engineering. Ocean Management, 3(2): 137–148, doi: 10.1016/0302-184X(77)90012-9 Grinyó J, Francescangeli M, Santín A, et al. 2022. Megafaunal assemblages in deep-sea ecosystems of the Gulf of Cadiz, northeast Atlantic Ocean. Deep-Sea Research Part I: Oceanographic Research Papers, 183: 103738, doi: 10.1016/j.dsr.2022.103738 Guo Binbin, Wang Weiqiang, Shu Yeqiang, et al. 2020. Observed deep anticyclonic cap over Caiwei Guyot. Journal of Geophysical Research: Oceans, 125(10): e2020JC016254, doi: 10.1029/2020JC016254 Hou Zhongsheng, Xu Jianxin. 2009. On data-driven control theory: the state of the art and perspective. Acta Automatica Sinica (in Chinese), 35(6): 650–667, doi: 10.3724/SP.J.1004.2009.00650 Jiang Xinbei, Gao Tianhan, Zhu Zichen, et al. 2021. Real-time face mask detection method based on YOLOv3. Electronics, 10(7): 837, doi: 10.3390/electronics10070837 Jobson D J, Rahman Z U, Woodell G A. 1997. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7): 965–976, doi: 10.1109/83.597272 Jocher G. 2020. Ultralytics YOLOv5. https://docs.ultralytics.com/zh/models/yolov5/#citations-and-acknowledgements Li Lifu, Liang Yi. 2021. Deep learning target vehicle detection method based on YOLOv3-tiny. In: Proceedings of 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference. Chongqing: IEEE, 1575–1579 Li Dong, Zhao Jun, Liu Chenggang et al. 2018. Advances of living environment characteristics and biogeochemical processes in the hadal zone. Earth Science (in Chinese), 43(S2): 162–178 Li Ting, Zhou Xianchun, Zhang Ying, et al. 2023. Underwater image enhancement based on IMSRCR and CLAHE-WGIF. Instrumentation, 10(2): 19–29 Liu Rulong, Wang Li, Wei Yuli, et al. 2018. The hadal biosphere: recent insights and new directions. Deep-Sea Research Part II: Topical Studies in Oceanography, 155: 11–18, doi: 10.1016/j.dsr2.2017.04.015 Liu Zhehao, Zhang Jianxing, Wang Bing, et al. 2023. Geomorphological characteristics and geological processes of Caroline M4 guyot. Oceanologia et Limnologia Sinica (in Chinese), 54(2): 351–361 Ma Li, Zhao Liya, Wang Zixuan, et al. 2023. Detection and counting of small target apples under complicated environments by using improved YOLOv7-tiny. Agronomy, 13(5): 1419, doi: 10.3390/agronomy13051419 Ottesen D, Dowdeswell J A, Bellec V K, et al. 2017. The geomorphic imprint of glacier surges into open-marine waters: examples from eastern Svalbard. Marine Geology, 392: 1–29, doi: 10.1016/j.margeo.2017.08.007 Perez J A A, Kitazato H, Sumida P Y G, et al. 2018. Benthopelagic megafauna assemblages of the Rio Grande rise (SW Atlantic). Deep-Sea Research Part I: Oceanographic Research Papers, 134: 1–11, doi: 10.1016/j.dsr.2018.03.001 Pizer S M, Amburn E P, Austin J D, et al. 1987. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3): 355–368 Reinhard E, Adhikhmin M, Gooch B, et al. 2001. Color transfer between images. IEEE Computer Graphics and Applications, 21(5): 34–41 Ren Shaoqing, He Kaiming, Girshick R, et al. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137–1149, doi: 10.1109/TPAMI.2016.2577031 Schönberger J L, Frahm J M. 2016. Structure-from-motion revisited. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 4104–4113 Usui A, Nishimura A, Iizasa K. 1993. Submersible observations of manganese nodule and crust deposits on the tenpo seamount, northwestern pacific. Marine Georesources & Geotechnology, 11(4): 263–291 Usui A, Sato H, Nishi K, et al. 2013. Geological characterization of co-rich ferromaganese crusts over the northwestern pacific seamounts. In: Proceedings of 2013 OCEANS. San Diego: IEEE, 1–3, doi: 10.23919/OCEANS.2013.6741019 Wang Fangjinhua, Galliani S, Vogel C, et al. 2021. PatchmatchNet: learned multi-view patchmatch stereo. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 14189–14198 Wang Chunzhi, Niu Hongxia. 2022. Sand-dust degraded image enhancement algorithm based on histogram equalization and MSRCR. Computer Engineering (in Chinese), 48(9): 223–229, doi: 10.19678/j.issn.1000-3428.0062764 Xie Jun, Di Jianglei, Qin Yuwen. 2022. Application of deep learning in underwater imaging (invited). Acta Photonica Sinica (in Chinese), 51(11): 1101001, doi: 10.3788/gzxb20225111.1101001 Yuan Yubin, Shen Yu, Peng Jing, et al. 2020. Defogging technology based on dual-channel sensor information fusion of near-infrared and visible light. Journal of Sensors, 2020(1): 8818650 Zhang Jianxing, Song Yongdong, Luan Zhendong, et al. 2021. Analysis of the characteristics of submarine topography and distribution of sediments near Juehua Island, Liaodong Bay. Marine Sciences (in Chinese), 45(9): 40–47 -

计量
- 文章访问数: 55
- HTML全文浏览量: 12
- 被引次数: 0