A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.
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| Title: | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis. |
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| Authors: | Bilodeau SM; Department of Biology, Wake Forest University, Winston-Salem, NC, United States of America.; Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC, United States of America., Schwartz AWH; Department of Biology, Wake Forest University, Winston-Salem, NC, United States of America.; Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC, United States of America., Xu B; Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States of America., Paúl Pauca V; Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States of America., Silman MR; Department of Biology, Wake Forest University, Winston-Salem, NC, United States of America.; Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC, United States of America. |
| Source: | PloS one [PLoS One] 2022 Feb 02; Vol. 17 (2), pp. e0263377. Date of Electronic Publication: 2022 Feb 02 (Print Publication: 2022). |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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