Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef
Abstract
:1. Introduction
2. Methods
2.1. Study Sites
2.2. Algorithm Development and Training
2.3. Model Application
2.4. Geospatial Analysis
3. Results
3.1. Algorithm Development and Training
3.2. Model Application
3.3. Geospatial Analysis
4. Discussion
4.1. Algorithm Development and Training
4.2. Model Application
4.3. Geospatial Analysis
4.4. Future Work
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Watson, S.A.; Neo, M.L. Conserving threatened species during rapid environmental change: Using biological responses to inform management strategies of giant clams. Conserv. Physiol. 2021, 9, coab082. [Google Scholar] [CrossRef] [PubMed]
- Neo, M.L.; Eckman, W.; Vicentuan, K.; Teo, S.L.-M.; Todd, P.A. The ecological significance of giant clams in coral reef ecosystems. Biol. Conserv. 2015, 181, 111–123. [Google Scholar] [CrossRef]
- Neo, M.L.; Wabnitz, C.C.C.; Braley, R.D.; Heslinga, G.A.; Fauvelot, C.; Van Wynsberge, S.; Andréfouët, S.; Waters, C.; Tan, A.S.-H.; Gomez, E.D.; et al. Giant Clams (Bivalvia: Cardiidae: Tridacninae): A Comprehensive Update of Species and Their Distribution, Current Threats and Conservation Status. Oceanogr. Mar. Biol. Annu. Rev. 2017, 55, 2–303. [Google Scholar]
- Mallela, J.; Perry, C. Calcium carbonate budgets for two coral reefs affected by different terrestrial runoff regimes, Rio Bueno, Jamaica. Coral Reefs 2007, 26, 129–145. [Google Scholar] [CrossRef]
- Rossbach, S.; Anton, A.; Duarte, C.M. Drivers of the abundance of Tridacna spp. Giant clams in the red sea. Front. Mar. Sci. 2021, 7, 592852. [Google Scholar] [CrossRef]
- Calumpong, H.P. The Giant Clam: An Ocean Culture Manual; Australian Centre for International Agricultural Research: Canberra, Australia, 1992. [Google Scholar]
- Govan, H.; Fabro, L.; Ropeti, E. Controlling Predators of Cultured Tridacnid Clams; ACIAR: Canberra, Australia, 1993. [Google Scholar]
- Klumpp, D.; Griffiths, C. Contributions of phototrophic and heterotrophic nutrition to the metabolic and growth requirements of four species of giant clam (Tridacnidae). Mar. Ecol. Prog. Ser. 1994, 115, 103–115. [Google Scholar] [CrossRef]
- Neo, M.L.; Todd, P.A. Conservation status reassessment of giant clams (Mollusca: Bivalvia: Tridacninae) in Singapore. Nat. Singap. 2013, 6, 125–133. [Google Scholar]
- Moore, D. Farming Giant Clams in 2021: A Great Future for the ‘Blue Economy’of Tropical Islands. In Aquaculture: Ocean Blue Carbon Meets UN-SDGS; Springer: Berlin/Heidelberg, Germany, 2022; pp. 131–153. [Google Scholar]
- Andréfouët, S.; Friedman, K.; Gilbert, A.; Remoissenet, G. A comparison of two surveys of invertebrates at Pacific Ocean islands: The giant clam at Raivavae Island, Australes Archipelago, French Polynesia. ICES J. Mar. Sci. 2009, 66, 1825–1836. [Google Scholar] [CrossRef]
- Andréfouët, S.; Gilbert, A.; Yan, L.; Remoissenet, G.; Payri, C.; Chancerelle, Y. The remarkable population size of the endangered clam Tridacna maxima assessed in Fangatau Atoll (Eastern Tuamotu, French Polynesia) using in situ and remote sensing data. ICES J. Mar. Sci. 2005, 62, 1037–1048. [Google Scholar] [CrossRef]
- Friedman, K.; Teitelbaum, A. Re-Introduction of Giant Clams in the Indo-Pacific. Global Reintroduction Perspectives: Re-Introduction Case-Studies from around the Globe; Soorae, P., Ed.; IUCN/SSC Re-Introduction Specialist Group: Abu Dhabi, United Arab Emirates, 2008; pp. 4–10. [Google Scholar]
- Gomez, E.D.; Mingoa-Licuanan, S.S. Achievements and lessons learned in restocking giant clams in the Philippines. Fish. Res. 2006, 80, 46–52. [Google Scholar] [CrossRef]
- Naguit, M.R.A.; Rehatta, B.M.; Calumpong, H.P.; Tisera, W.L. Ecology and genetic structure of giant clams around Savu Sea, East Nusa Tenggara province, Indonesia. Asian J. Biodivers. 2013, 3, 174–194. [Google Scholar] [CrossRef]
- Ramah, S.; Taleb-Hossenkhan, N.; Todd, P.A.; Neo, M.L.; Bhagooli, R. Drastic decline in giant clams (Bivalvia: Tridacninae) around Mauritius Island, Western Indian Ocean: Implications for conservation and management. Mar. Biodivers. 2019, 49, 815–823. [Google Scholar] [CrossRef]
- Joyce, K.E.; Anderson, K.; Bartolo, R.E. Of Course We Fly Unmanned—We’re Women! Drones 2021, 5, 21. [Google Scholar] [CrossRef]
- Chabot, D.; Dillon, C.; Ahmed, O.; Shemrock, A. Object-based analysis of UAS imagery to map emergent and submerged invasive aquatic vegetation: A case study. J. Unmanned Veh. Syst. 2016, 5, 27–33. [Google Scholar] [CrossRef]
- Drever, M.C.; Chabot, D.; O’Hara, P.D.; Thomas, J.D.; Breault, A.; Millikin, R.L. Evaluation of an unmanned rotorcraft to monitor wintering waterbirds and coastal habitats in British Columbia, Canada. J. Unmanned Veh. Syst. 2015, 3, 256–267. [Google Scholar] [CrossRef]
- Kelaher, B.P.; Peddemors, V.M.; Hoade, B.; Colefax, A.P.; Butcher, P.A. Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys. J. Unmanned Veh. Syst. 2019, 8, 30–43. [Google Scholar] [CrossRef]
- Pomeroy, P.; O’connor, L.; Davies, P. Assessing use of and reaction to unmanned aerial systems in gray and harbor seals during breeding and molt in the UK. J. Unmanned Veh. Syst. 2015, 3, 102–113. [Google Scholar] [CrossRef]
- Oleksyn, S.; Tosetto, L.; Raoult, V.; Joyce, K.E.; Williamson, J.E. Going Batty: The Challenges and Opportunities of Using Drones to Monitor the Behaviour and Habitat Use of Rays. Drones 2021, 5, 12. [Google Scholar] [CrossRef]
- Williamson, J.E.; Duce, S.; Joyce, K.E.; Raoult, V. Putting sea cucumbers on the map: Projected holothurian bioturbation rates on a coral reef scale. Coral Reefs 2021, 40, 559–569. [Google Scholar] [CrossRef]
- Joyce, K.E.; Fickas, K.C.; Kalamandeen, M. The unique value proposition for using drones to map coastal ecosystems. Camb. Prism. Coast. Futures 2023, 1, e6. [Google Scholar] [CrossRef]
- Badawy, M.; Direkoglu, C. Sea turtle detection using faster r-cnn for conservation purpose. In Proceedings of the 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions-ICSCCW-2019, Prague, Czech Republic, 27–28 August 2019; pp. 535–541. [Google Scholar]
- Dujon, A.M.; Ierodiaconou, D.; Geeson, J.J.; Arnould, J.P.; Allan, B.M.; Katselidis, K.A.; Schofield, G. Machine learning to detect marine animals in UAV imagery: Effect of morphology, spacing, behaviour and habitat. Remote Sens. Ecol. Conserv. 2021, 7, 341–354. [Google Scholar] [CrossRef]
- Gray, P.C.; Chamorro, D.F.; Ridge, J.T.; Kerner, H.R.; Ury, E.A.; Johnston, D.W. Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time. Remote Sens. 2021, 13, 3953. [Google Scholar] [CrossRef]
- Gray, P.C.; Fleishman, A.B.; Klein, D.J.; McKown, M.W.; Bezy, V.S.; Lohmann, K.J.; Johnston, D.W. A convolutional neural network for detecting sea turtles in drone imagery. Methods Ecol. Evol. 2019, 10, 345–355. [Google Scholar] [CrossRef]
- Hopkinson, B.M.; King, A.C.; Owen, D.P.; Johnson-Roberson, M.; Long, M.H.; Bhandarkar, S.M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS ONE 2020, 15, e0230671. [Google Scholar] [CrossRef] [PubMed]
- Li, J.Y.; Duce, S.; Joyce, K.E.; Xiang, W. SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats. Drones 2021, 5, 28. [Google Scholar] [CrossRef]
- Saqib, M.; Khan, S.D.; Sharma, N.; Scully-Power, P.; Butcher, P.; Colefax, A.; Blumenstein, M. Real-time drone surveillance and population estimation of marine animals from aerial imagery. In Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand, 19–21 November 2018; pp. 1–6. [Google Scholar]
- Harasyn, M.L.; Chan, W.S.; Ausen, E.L.; Barber, D.G. Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning. Drone Syst. Appl. 2022, 10, 77–96. [Google Scholar] [CrossRef]
- Barbedo, J.G.A.; Koenigkan, L.V.; Santos, T.T.; Santos, P.M. A study on the detection of cattle in UAV images using deep learning. Sensors 2019, 19, 5436. [Google Scholar] [CrossRef]
- Borowicz, A.; Le, H.; Humphries, G.; Nehls, G.; Höschle, C.; Kosarev, V.; Lynch, H.J. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS ONE 2019, 14, e0212532. [Google Scholar] [CrossRef]
- Green, K.M.; Virdee, M.K.; Cubaynes, H.C.; Aviles-Rivero, A.I.; Fretwell, P.T.; Gray, P.C.; Johnston, D.W.; Schönlieb, C.B.; Torres, L.G.; Jackson, J.A. Gray whale detection in satellite imagery using deep learning. Remote Sens. Ecol. Conserv. 2023, 9, 829–840. [Google Scholar] [CrossRef]
- Nategh, M.N.; Zgaren, A.; Bouachir, W.; Bouguila, N. Automatic counting of mounds on UAV images: Combining instance segmentation and patch-level correction. In Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas, 12–14 December 2022; pp. 375–381. [Google Scholar]
- Psiroukis, V.; Espejo-Garcia, B.; Chitos, A.; Dedousis, A.; Karantzalos, K.; Fountas, S. Assessment of different object detectors for the maturity level classification of broccoli crops using uav imagery. Remote Sens. 2022, 14, 731. [Google Scholar] [CrossRef]
- Puliti, S.; Astrup, R. Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery. Int. J. Appl. Earth Obs. Geoinf. 2022, 112, 102946. [Google Scholar] [CrossRef]
- Veeranampalayam Sivakumar, A.N. Mid to Late Season Weed Detection in Soybean Production Fields Using Unmanned Aerial Vehicle and Machine Learning; University of Nebraska: Lincoln, NE, USA, 2019. [Google Scholar]
- Yildirim, E.; Nazar, M.; Sefercik, U.G.; Kavzoglu, T. Stone Pine (Pinus pinea L.) Detection from High-Resolution UAV Imagery Using Deep Learning Model. In Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 441–444. [Google Scholar]
- Hosang, J.; Benenson, R.; Schiele, B. Learning non-maximum suppression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4507–4515. [Google Scholar]
- Van Etten, A. You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv 2018, arXiv:1805.09512. [Google Scholar]
- Hofinger, P.; Klemmt, H.-J.; Ecke, S.; Rogg, S.; Dempewolf, J. Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data. Remote Sens. 2023, 15, 1964. [Google Scholar] [CrossRef]
- Akyon, F.C.; Altinuc, S.O.; Temizel, A. Slicing aided hyper inference and fine-tuning for small object detection. In Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 16–19 October 2022; pp. 966–970. [Google Scholar]
- Varga, L. Identifying Anthropogenic Pressure on Beach Vegetation by Means of Detecting and Counting Footsteps on UAV Images. Master’s Thesis, Utrecht University, Utrecht, The Netherlands, 2023. [Google Scholar]
- Lucas, J.; Lindsay, S.; Braley, R.; Whitford, J. Density of clams and depth reduce growth in grow-out culture of Tridacna gigas. In ACIAR Proceedings: Proceedings of the 7th International Coral Reef Symposium, Guam, Micronesia, 21–26 June 1992; Australian Centre for International Agricultural Research: Canberra, Australia, 1993; p. 67. [Google Scholar]
- Moorhead, A. Giant clam aquaculture in the Pacific region: Perceptions of value and impact. Dev. Pract. 2018, 28, 624–635. [Google Scholar] [CrossRef]
- Graham, E. Orpheus Clam Farm 2022. Available online: https://data.geonadir.com/image-collection-details/1495?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Bektas, T. Pioneer Bay, Orpheus Island Clam Gardens Section. Available online: https://data.geonadir.com/image-collection-details/963?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Bektas, T. Pioneer Bay Clam Gardens. Available online: https://data.geonadir.com/image-collection-details/964?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Bektas, T. Clam Gardens/Coastal Substrate. Available online: https://data.geonadir.com/image-collection-details/652?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Joyce, K.E.; Li, J.Y. Ribbon 5 North Oct 2021. Available online: https://data.geonadir.com/image-collection-details/457?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Joyce, K.E.; Li, J.Y. Ribbon 5 Middle Oct 2021. Available online: https://data.geonadir.com/image-collection-details/456?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Joyce, K.E.; Li, J.Y. West Hastings Reef Flat. Available online: https://data.geonadir.com/image-collection-details/1560?workspace=37b63ceb-e6c1-45d2-afa9-0e6dabc03a49-8369 (accessed on 15 August 2024).
- Clark, J.A. Pillow (PIL Fork) 10.4.0 Documentation. Available online: https://pillow.readthedocs.io/en/stable/ (accessed on 15 August 2024).
- Ozge Unel, F.; Ozkalayci, B.O.; Cigla, C. The power of tiling for small object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, 16–17 June 2019. [Google Scholar]
- Kaur, P.; Khehra, B.S.; Mavi, E.B.S. Data Augmentation for Object Detection: A Review. In Proceedings of the 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Lansing, MI, USA, 9–11 August 2021; pp. 537–543. [Google Scholar]
- Suto, J. Improving the generalization capability of YOLOv5 on remote sensed insect trap images with data augmentation. Multimed. Tools Appl. 2024, 83, 27921–27934. [Google Scholar] [CrossRef]
- Norouzzadeh, M.S.; Nguyen, A.; Kosmala, M.; Swanson, A.; Palmer, M.S.; Packer, C.; Clune, J. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. USA 2018, 115, E5716–E5725. [Google Scholar] [CrossRef]
- Everingham, M.; Van Gool, L.; Williams, C.K.; Winn, J.; Zisserman, A. The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 2010, 88, 303–338. [Google Scholar] [CrossRef]
- Reinke, A.; Tizabi, M.D.; Baumgartner, M.; Eisenmann, M.; Heckmann-Nötzel, D.; Kavur, A.E.; Rädsch, T.; Sudre, C.H.; Acion, L.; Antonelli, M. Understanding metric-related pitfalls in image analysis validation. arXiv 2023, arXiv:2302.01790. [Google Scholar] [CrossRef]
- Reinke, A.; Tizabi, M.D.; Sudre, C.H.; Eisenmann, M.; Rädsch, T.; Baumgartner, M.; Acion, L.; Antonelli, M.; Arbel, T.; Bakas, S. Common limitations of image processing metrics: A picture story. arXiv 2021, arXiv:2104.05642. [Google Scholar]
- Zhou, H.; Jiang, F.; Lu, H. SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain object detection. Comput. Vis. Image Underst. 2023, 229, 103649. [Google Scholar] [CrossRef]
- Zhao, T.; Zhang, G.; Zhong, P.; Shen, Z. DMDnet: A decoupled multi-scale discriminant model for cross-domain fish detection. Biosyst. Eng. 2023, 234, 32–45. [Google Scholar] [CrossRef]
- Li, C.; Yan, H.; Qian, X.; Zhu, S.; Zhu, P.; Liao, C.; Tian, H.; Li, X.; Wang, X.; Li, X. A domain adaptation YOLOv5 model for industrial defect inspection. Measurement 2023, 213, 112725. [Google Scholar] [CrossRef]
- Lai, J.; Liang, Y.; Kuang, Y.; Xie, Z.; He, H.; Zhuo, Y.; Huang, Z.; Zhu, S.; Huang, Z. IO-YOLOv5: Improved Pig Detection under Various Illuminations and Heavy Occlusion. Agriculture 2023, 13, 1349. [Google Scholar] [CrossRef]
- Kim, J.; Huh, J.; Park, I.; Bak, J.; Kim, D.; Lee, S. Small object detection in infrared images: Learning from imbalanced cross-domain data via domain adaptation. Appl. Sci. 2022, 12, 11201. [Google Scholar] [CrossRef]
- Gheisari, M.; Baghshah, M.S. Joint predictive model and representation learning for visual domain adaptation. Eng. Appl. Artif. Intell. 2017, 58, 157–170. [Google Scholar] [CrossRef]
- Blight, L.K.; Bertram, D.F.; Kroc, E. Evaluating UAV-based techniques to census an urban-nesting gull population on Canada’s Pacific coast. J. Unmanned Veh. Syst. 2019, 7, 312–324. [Google Scholar] [CrossRef]
- Charry, B.; Tissier, E.; Iacozza, J.; Marcoux, M.; Watt, C.A. Mapping Arctic cetaceans from space: A case study for beluga and narwhal. PLoS ONE 2021, 16, e0254380. [Google Scholar] [CrossRef] [PubMed]
- Oosthuizen, W.C.; Krüger, L.; Jouanneau, W.; Lowther, A.D. Unmanned aerial vehicle (UAV) survey of the Antarctic shag (Leucocarbo bransfieldensis) breeding colony at Harmony Point, Nelson Island, South Shetland Islands. Polar Biol. 2020, 43, 187–191. [Google Scholar] [CrossRef]
- Cubaynes, H.C.; Fretwell, P.T.; Bamford, C.; Gerrish, L.; Jackson, J.A. Whales from space: Four mysticete species described using new VHR satellite imagery. Mar. Mammal Sci. 2019, 35, 466–491. [Google Scholar] [CrossRef]
- Hodgson, J.C.; Baylis, S.M.; Mott, R.; Herrod, A.; Clarke, R.H. Precision wildlife monitoring using unmanned aerial vehicles. Sci. Rep. 2016, 6, 22574. [Google Scholar] [CrossRef]
- Zhang, L.; Zhou, X.; Li, B.; Zhang, H.; Duan, Q. Automatic shrimp counting method using local images and lightweight YOLOv4. Biosyst. Eng. 2022, 220, 39–54. [Google Scholar] [CrossRef]
- Lyons, M.B.; Brandis, K.J.; Murray, N.J.; Wilshire, J.H.; McCann, J.A.; Kingsford, R.T.; Callaghan, C.T. Monitoring large and complex wildlife aggregations with drones. Methods Ecol. Evol. 2019, 10, 1024–1035. [Google Scholar] [CrossRef]
- Bomantara, Y.A.; Mustafa, H.; Bartholomeus, H.; Kooistra, L. Detection of Artificial Seed-like Objects from UAV Imagery. Remote Sens. 2023, 15, 1637. [Google Scholar] [CrossRef]
- Natesan, S.; Armenakis, C.; Vepakomma, U. Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV. J. Unmanned Veh. Syst. 2020, 8, 310–333. [Google Scholar] [CrossRef]
- Braley, R.D. A population study of giant clams (Tridacninae) on the Great Barrier Reef over three-decades. Molluscan Res. 2023, 43, 77–95. [Google Scholar] [CrossRef]
- Joyce, K.; Duce, S.; Leahy, S.; Leon, J.; Maier, S. Principles and practice of acquiring drone-based image data in marine environments. Mar. Freshw. Res. 2018, 70, 952–963. [Google Scholar] [CrossRef]
Images from Ortho 1 | Images from Orthos 2–5 | Augmented Images | Null Images | Total Images | Tile Size (Pixels) | |
---|---|---|---|---|---|---|
1 | 100 | 0 | 0 | 0 | 100 | 640 × 640 |
2 | 100 | 0 | 0 | 0 | 100 | 840 × 840 |
3 | 100 | 0 | 140 | 0 | 240 | 640 × 640 |
4 | 100 | 0 | 140 | 0 | 240 | 840 × 840 |
5 | 100 | 200 | 0 | 0 | 300 | 640 × 640 |
6 | 100 | 200 | 0 | 0 | 300 | 840 × 840 |
7 | 100 | 200 | 420 | 0 | 720 | 640 × 640 |
8 | 100 | 200 | 420 | 0 | 720 | 840 × 840 |
9 | 100 + 100 | 200 + 200 | 0 | 0 | 600 | 50% each: 640 × 640 832 × 832 |
10 | 100 + 100 | 200 + 200 | 420 + 420 | 0 | 1440 | 50% each: 640 × 640 832 × 832 |
11 | 100 + 100 | 200 + 200 | 420 + 420 | 200 | 1640 | 50% each: 640 × 640 832 × 832 |
Component | Specification |
---|---|
CPU | 12th Gen Intel® Core (TM) (Santa Clara, CA, USA) i9-12900HK, 2500 MHz, 14 Core(s), 20 Logical Processor(s) |
GPU | NVIDIA (Santa Clara, CA, USA) GeForce RTX 3050 Ti Laptop GPU |
Memory | 64 GB at 4800 MHz speed |
Library | Version |
---|---|
python | 3.10.13 |
torch | 2.1.0 |
sahi | 0.11.14 |
Yolov5 | 7.0.12 |
fiftyone | 0.22.1 |
openpyxl | 3.1.2 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Decitre, O.; Joyce, K.E. Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef. Drones 2024, 8, 458. https://doi.org/10.3390/drones8090458
Decitre O, Joyce KE. Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef. Drones. 2024; 8(9):458. https://doi.org/10.3390/drones8090458
Chicago/Turabian StyleDecitre, Olivier, and Karen E. Joyce. 2024. "Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef" Drones 8, no. 9: 458. https://doi.org/10.3390/drones8090458