Changes
On May 19, 2022 at 5:56:08 PM CDT, Claire Herbert:
-
Changed value of field
keywords
toBeluga,Unmanned Aerial Vehicle,computer vision,deep learning,object detection,object tracking
in Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning -
Changed value of field
related_datasets
to["b5f259b4-3ace-4750-bfb0-47c4e794082f"]
in Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning
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2 | "Author": [ | 2 | "Author": [ | ||
3 | { | 3 | { | ||
4 | "affiliation": "Centre for Earth Observation Science - | 4 | "affiliation": "Centre for Earth Observation Science - | ||
5 | University of Manitoba", | 5 | University of Manitoba", | ||
6 | "creatorName": "Harasyn, Madison", | 6 | "creatorName": "Harasyn, Madison", | ||
7 | "email": "Madison.harasyn@umanitoba.ca", | 7 | "email": "Madison.harasyn@umanitoba.ca", | ||
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14 | "affiliation": "Centre for Earth Observation Science - | 14 | "affiliation": "Centre for Earth Observation Science - | ||
15 | University of Manitoba", | 15 | University of Manitoba", | ||
16 | "creatorName": "Chan, Wayne", | 16 | "creatorName": "Chan, Wayne", | ||
17 | "email": "wayne.chan@umanitoba.ca", | 17 | "email": "wayne.chan@umanitoba.ca", | ||
18 | "nameIdentifier": "", | 18 | "nameIdentifier": "", | ||
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20 | }, | 20 | }, | ||
21 | { | 21 | { | ||
22 | "affiliation": "Centre for Earth Observation Science - | 22 | "affiliation": "Centre for Earth Observation Science - | ||
23 | University of Manitoba", | 23 | University of Manitoba", | ||
24 | "creatorName": "Ausen, Emma", | 24 | "creatorName": "Ausen, Emma", | ||
25 | "email": "emma.ausen@umanitoba.ca", | 25 | "email": "emma.ausen@umanitoba.ca", | ||
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28 | }, | 28 | }, | ||
29 | { | 29 | { | ||
30 | "affiliation": "Centre for Earth Observation Science - | 30 | "affiliation": "Centre for Earth Observation Science - | ||
31 | University of Manitoba", | 31 | University of Manitoba", | ||
32 | "creatorName": "Barber, David", | 32 | "creatorName": "Barber, David", | ||
33 | "email": "david.barber@umanitoba.ca", | 33 | "email": "david.barber@umanitoba.ca", | ||
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38 | } | 38 | } | ||
39 | ], | 39 | ], | ||
40 | "Identifier": "10.1139/juvs-2021-0024", | 40 | "Identifier": "10.1139/juvs-2021-0024", | ||
41 | "PublicationYear": "2022", | 41 | "PublicationYear": "2022", | ||
42 | "Publisher": "Drone Systems and Applications", | 42 | "Publisher": "Drone Systems and Applications", | ||
43 | "ResourceType": "journal article", | 43 | "ResourceType": "journal article", | ||
44 | "Rights": "Creative Commons Attribution 4.0 International", | 44 | "Rights": "Creative Commons Attribution 4.0 International", | ||
45 | "Version": "1.0", | 45 | "Version": "1.0", | ||
46 | "author": null, | 46 | "author": null, | ||
47 | "author_email": null, | 47 | "author_email": null, | ||
48 | "awardTitle": "The Canada Excellence Research Chair (CERC) and the | 48 | "awardTitle": "The Canada Excellence Research Chair (CERC) and the | ||
49 | Canada Research Chair (CRC programs)", | 49 | Canada Research Chair (CRC programs)", | ||
50 | "awardURI": "https://www.cerc.gc.ca/", | 50 | "awardURI": "https://www.cerc.gc.ca/", | ||
51 | "citation": "Madison L.Harasyn, Wayne S.Chan, Emma L.Ausen, and | 51 | "citation": "Madison L.Harasyn, Wayne S.Chan, Emma L.Ausen, and | ||
52 | David G.Barber. Detection and tracking of belugas, kayaks and | 52 | David G.Barber. Detection and tracking of belugas, kayaks and | ||
53 | motorized boats in drone video using deep learning. Drone Systems and | 53 | motorized boats in drone video using deep learning. Drone Systems and | ||
54 | Applications. 10(1): 77-96. https://doi.org/10.1139/juvs-2021-0024", | 54 | Applications. 10(1): 77-96. https://doi.org/10.1139/juvs-2021-0024", | ||
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56 | "descriptionType": "Abstract", | 56 | "descriptionType": "Abstract", | ||
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65 | Photo and Video is licensed under CC BY 2.0", | 65 | their characteristics. Examples of data you can find here include | ||
66 | river and lake data, water quality data. \r\n\r\nIn CEOS, related | ||||
67 | research themes include biogeochemistry, Inland lakes and waters, | ||||
68 | modelling, remote sensing and technology, trace metals and | ||||
69 | contaminants.", | ||||
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81 | physical oceanography, remote sensing and technology and trace metals | ||||
82 | and contaminants", | ||||
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n | 76 | "keywords": "Unmanned Aerial Vehicle,computer vision,deep | n | 93 | "keywords": "Beluga,Unmanned Aerial Vehicle,computer vision,deep |
77 | learning,object detection,object tracking,Beluga", | 94 | learning,object detection,object tracking", | ||
78 | "language": "English", | 95 | "language": "English", | ||
79 | "licenceType": "Open", | 96 | "licenceType": "Open", | ||
80 | "license_id": null, | 97 | "license_id": null, | ||
81 | "license_title": null, | 98 | "license_title": null, | ||
82 | "maintainer": null, | 99 | "maintainer": null, | ||
83 | "maintainer_email": null, | 100 | "maintainer_email": null, | ||
84 | "metadata_created": "2022-04-07T19:45:13.021227", | 101 | "metadata_created": "2022-04-07T19:45:13.021227", | ||
n | 85 | "metadata_modified": "2022-05-10T21:54:18.642115", | n | 102 | "metadata_modified": "2022-05-19T22:56:08.496324", |
86 | "name": | 103 | "name": | ||
87 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | 104 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | ||
88 | "notes": "Aerial imagery surveys are commonly used in marine mammal | 105 | "notes": "Aerial imagery surveys are commonly used in marine mammal | ||
89 | research to determine population size, distribution and habitat use. | 106 | research to determine population size, distribution and habitat use. | ||
90 | Analysis of aerial photos involves hours of manually identifying | 107 | Analysis of aerial photos involves hours of manually identifying | ||
91 | individuals present in each image and converting raw counts into | 108 | individuals present in each image and converting raw counts into | ||
92 | useable biological statistics. Our research proposes the use of deep | 109 | useable biological statistics. Our research proposes the use of deep | ||
93 | learning algorithms to increase the efficiency of the marine mammal | 110 | learning algorithms to increase the efficiency of the marine mammal | ||
94 | research workflow. To test the feasibility of this proposal, the | 111 | research workflow. To test the feasibility of this proposal, the | ||
95 | existing YOLOv4 convolutional neural network model was trained to | 112 | existing YOLOv4 convolutional neural network model was trained to | ||
96 | detect belugas, kayaks and motorized boats in oblique drone imagery, | 113 | detect belugas, kayaks and motorized boats in oblique drone imagery, | ||
97 | collected from a stationary tethered system. Automated computer-based | 114 | collected from a stationary tethered system. Automated computer-based | ||
98 | object detection achieved the following precision and recall, | 115 | object detection achieved the following precision and recall, | ||
99 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | 116 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | ||
100 | kayak = 96%/96%. We then tested the performance of computer vision | 117 | kayak = 96%/96%. We then tested the performance of computer vision | ||
101 | tracking of belugas and occupied watercraft in drone videos using the | 118 | tracking of belugas and occupied watercraft in drone videos using the | ||
102 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | 119 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | ||
103 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | 120 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | ||
104 | precision (MOTP) between 63% and 86%. Results from this research | 121 | precision (MOTP) between 63% and 86%. Results from this research | ||
105 | indicate that deep learning technology can detect and track features | 122 | indicate that deep learning technology can detect and track features | ||
106 | more consistently than human annotators, allowing for larger datasets | 123 | more consistently than human annotators, allowing for larger datasets | ||
107 | to be processed within a fraction of the time while avoiding | 124 | to be processed within a fraction of the time while avoiding | ||
108 | discrepancies introduced by labeling fatigue or multiple human | 125 | discrepancies introduced by labeling fatigue or multiple human | ||
109 | annotators.", | 126 | annotators.", | ||
110 | "num_resources": 1, | 127 | "num_resources": 1, | ||
111 | "num_tags": 6, | 128 | "num_tags": 6, | ||
112 | "organization": { | 129 | "organization": { | ||
113 | "approval_status": "approved", | 130 | "approval_status": "approved", | ||
114 | "created": "2017-07-21T13:15:49.935872", | 131 | "created": "2017-07-21T13:15:49.935872", | ||
115 | "description": "The Centre for Earth Observation Science (CEOS) | 132 | "description": "The Centre for Earth Observation Science (CEOS) | ||
116 | was established in 1994 with a mandate to research, preserve and | 133 | was established in 1994 with a mandate to research, preserve and | ||
117 | communicate knowledge of Earth system processes using the technologies | 134 | communicate knowledge of Earth system processes using the technologies | ||
118 | of Earth Observation Science. Research is multidisciplinary and | 135 | of Earth Observation Science. Research is multidisciplinary and | ||
119 | collaborative seeking to understand the complex interrelationships | 136 | collaborative seeking to understand the complex interrelationships | ||
120 | between elements of Earth systems, and how these systems will likely | 137 | between elements of Earth systems, and how these systems will likely | ||
121 | respond to climate change. Although researchers have worked in many | 138 | respond to climate change. Although researchers have worked in many | ||
122 | regions, the Arctic marine system has always been a unifying focus of | 139 | regions, the Arctic marine system has always been a unifying focus of | ||
123 | activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate | 140 | activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate | ||
124 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | 141 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | ||
125 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | 142 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | ||
126 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | 143 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | ||
127 | existing research activity are divided among key themes:\r\n\r\nArctic | 144 | existing research activity are divided among key themes:\r\n\r\nArctic | ||
128 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | 145 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | ||
129 | preservation, archaeo-geophysics, permafrost degredation, lithic | 146 | preservation, archaeo-geophysics, permafrost degredation, lithic | ||
130 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | 147 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | ||
131 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | 148 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | ||
132 | ethnographic analogy, traditional knowledge, climate change and | 149 | ethnographic analogy, traditional knowledge, climate change and | ||
133 | northern heritage resource management.\r\n\r\nAtmospheric | 150 | northern heritage resource management.\r\n\r\nAtmospheric | ||
134 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | 151 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | ||
135 | extreme weather, circulation, eddy correlations, polar vortex, | 152 | extreme weather, circulation, eddy correlations, polar vortex, | ||
136 | climate, teleconnections, geophysical fluid dynamics, flux and energy | 153 | climate, teleconnections, geophysical fluid dynamics, flux and energy | ||
137 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | 154 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | ||
138 | albedo feedback, cloud radiative forcing, pCO2. | 155 | albedo feedback, cloud radiative forcing, pCO2. | ||
139 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | 156 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | ||
140 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | 157 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | ||
141 | metals, minerals, hydrocarbons, brine processes, otolith | 158 | metals, minerals, hydrocarbons, brine processes, otolith | ||
142 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | 159 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | ||
143 | trace metals, PAHs, source, transport, transformation, pathways, | 160 | trace metals, PAHs, source, transport, transformation, pathways, | ||
144 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | 161 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | ||
145 | Observation Science: Active and passive microwave, LiDAR, EM | 162 | Observation Science: Active and passive microwave, LiDAR, EM | ||
146 | induction, spatial-temporal analysis, forward and inverse scattering | 163 | induction, spatial-temporal analysis, forward and inverse scattering | ||
147 | models, complex permittivity, ocean colour, ocean surface roughness, | 164 | models, complex permittivity, ocean colour, ocean surface roughness, | ||
148 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | 165 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | ||
149 | Biophysical processes, primary production; ice algae, ice | 166 | Biophysical processes, primary production; ice algae, ice | ||
150 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | 167 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | ||
151 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | 168 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | ||
152 | transport, nutrient transport, contaminants, landscape processes, | 169 | transport, nutrient transport, contaminants, landscape processes, | ||
153 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | 170 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | ||
154 | whales, habitat, conservation, satellite telemetry, distribution, | 171 | whales, habitat, conservation, satellite telemetry, distribution, | ||
155 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | 172 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | ||
156 | Simulation of sea ice and oceanic regional processes, Nucleus for | 173 | Simulation of sea ice and oceanic regional processes, Nucleus for | ||
157 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | 174 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | ||
158 | interactions, hind cast simulations and projections for sea ice state | 175 | interactions, hind cast simulations and projections for sea ice state | ||
159 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | 176 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | ||
160 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | 177 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | ||
161 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | 178 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | ||
162 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | 179 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | ||
163 | processes, extreme ice features and hazards, snow, ridges, | 180 | processes, extreme ice features and hazards, snow, ridges, | ||
164 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | 181 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | ||
165 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | 182 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | ||
166 | hunting, food security, sea ice use, community-based research, | 183 | hunting, food security, sea ice use, community-based research, | ||
167 | community-based monitoring, two ways of knowing.", | 184 | community-based monitoring, two ways of knowing.", | ||
168 | "id": "9e21f6b6-d13f-4ba2-a379-fd962f507071", | 185 | "id": "9e21f6b6-d13f-4ba2-a379-fd962f507071", | ||
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170 | "is_organization": true, | 187 | "is_organization": true, | ||
171 | "name": "ceos2", | 188 | "name": "ceos2", | ||
172 | "state": "active", | 189 | "state": "active", | ||
173 | "title": "CEOS", | 190 | "title": "CEOS", | ||
174 | "type": "organization" | 191 | "type": "organization" | ||
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190 | "related_programs": "[]", | 196 | "related_programs": "[]", | ||
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200 | "description": "Churchill Beluga Boat Drone Imagery related | 206 | "description": "Churchill Beluga Boat Drone Imagery related | ||
201 | journal article published in Drone Systems and Applications.\r\nDOI: | 207 | journal article published in Drone Systems and Applications.\r\nDOI: | ||
202 | https://doi.org/10.1139/juvs-2021-0024", | 208 | https://doi.org/10.1139/juvs-2021-0024", | ||
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210 | "name": "Detection and tracking of belugas, kayaks and motorized | 216 | "name": "Detection and tracking of belugas, kayaks and motorized | ||
211 | boats in drone video using deep learning", | 217 | boats in drone video using deep learning", | ||
212 | "package_id": "54b0d7a1-8536-4d40-b1bb-daad81805f43", | 218 | "package_id": "54b0d7a1-8536-4d40-b1bb-daad81805f43", | ||
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217 | "state": "active", | 223 | "state": "active", | ||
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219 | rce/5bcbb0bc-425b-4fad-b7ff-4c8599043dcf/download/juvs-2021-0024.pdf", | 225 | rce/5bcbb0bc-425b-4fad-b7ff-4c8599043dcf/download/juvs-2021-0024.pdf", | ||
220 | "url_type": "upload" | 226 | "url_type": "upload" | ||
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224 | "rightsIdentifierScheme": "SPDX", | 230 | "rightsIdentifierScheme": "SPDX", | ||
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231 | { | 237 | { | ||
232 | "display_name": "Beluga", | 238 | "display_name": "Beluga", | ||
233 | "id": "a9f25a89-b0ef-4d4d-993d-73f28e0d702a", | 239 | "id": "a9f25a89-b0ef-4d4d-993d-73f28e0d702a", | ||
234 | "name": "Beluga", | 240 | "name": "Beluga", | ||
235 | "state": "active", | 241 | "state": "active", | ||
236 | "vocabulary_id": null | 242 | "vocabulary_id": null | ||
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240 | "id": "a6dc9001-e6da-4a84-bfec-2941d3ebce78", | 246 | "id": "a6dc9001-e6da-4a84-bfec-2941d3ebce78", | ||
241 | "name": "Unmanned Aerial Vehicle", | 247 | "name": "Unmanned Aerial Vehicle", | ||
242 | "state": "active", | 248 | "state": "active", | ||
243 | "vocabulary_id": null | 249 | "vocabulary_id": null | ||
244 | }, | 250 | }, | ||
245 | { | 251 | { | ||
246 | "display_name": "computer vision", | 252 | "display_name": "computer vision", | ||
247 | "id": "d7270905-c420-4d19-aa9c-c6f818ab5b67", | 253 | "id": "d7270905-c420-4d19-aa9c-c6f818ab5b67", | ||
248 | "name": "computer vision", | 254 | "name": "computer vision", | ||
249 | "state": "active", | 255 | "state": "active", | ||
250 | "vocabulary_id": null | 256 | "vocabulary_id": null | ||
251 | }, | 257 | }, | ||
252 | { | 258 | { | ||
253 | "display_name": "deep learning", | 259 | "display_name": "deep learning", | ||
254 | "id": "87526358-2d8a-4c78-8375-38c132b53d5a", | 260 | "id": "87526358-2d8a-4c78-8375-38c132b53d5a", | ||
255 | "name": "deep learning", | 261 | "name": "deep learning", | ||
256 | "state": "active", | 262 | "state": "active", | ||
257 | "vocabulary_id": null | 263 | "vocabulary_id": null | ||
258 | }, | 264 | }, | ||
259 | { | 265 | { | ||
260 | "display_name": "object detection", | 266 | "display_name": "object detection", | ||
261 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | 267 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | ||
262 | "name": "object detection", | 268 | "name": "object detection", | ||
263 | "state": "active", | 269 | "state": "active", | ||
264 | "vocabulary_id": null | 270 | "vocabulary_id": null | ||
265 | }, | 271 | }, | ||
266 | { | 272 | { | ||
267 | "display_name": "object tracking", | 273 | "display_name": "object tracking", | ||
268 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | 274 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | ||
269 | "name": "object tracking", | 275 | "name": "object tracking", | ||
270 | "state": "active", | 276 | "state": "active", | ||
271 | "vocabulary_id": null | 277 | "vocabulary_id": null | ||
272 | } | 278 | } | ||
273 | ], | 279 | ], | ||
274 | "theme": [ | 280 | "theme": [ | ||
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276 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | 282 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | ||
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278 | ], | 284 | ], | ||
279 | "title": "Detection and tracking of belugas, kayaks and motorized | 285 | "title": "Detection and tracking of belugas, kayaks and motorized | ||
280 | boats in drone video using deep learning", | 286 | boats in drone video using deep learning", | ||
281 | "type": "publication", | 287 | "type": "publication", | ||
282 | "url": null, | 288 | "url": null, | ||
283 | "version": null | 289 | "version": null | ||
284 | } | 290 | } |