HOW MANY HIPPOS (HOMHIP): ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV

41 citations as recorded by crossref.

1. Computer-automated bird detection and counts in high-resolution aerial images: a review D. Chabot & C. Francis 10.1111/jofo.12171
2. Estimating kangaroo density by aerial survey: a comparison of thermal cameras with human observers M. Lethbridge et al. 10.1071/WR18122
3. Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors Z. Delisle et al. 10.1002/rse2.305
4. Identification of salmon redds using RPV‐based imagery produces comparable estimates to ground counts with high inter‐observer variability D. Auerbach & A. Fremier 10.1002/rra.4065
5. Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery L. Al-Shimaysawee et al. 10.3390/s24217048
6. Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors R. Kays et al. 10.1080/01431161.2018.1523580
7. Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land M. Attard et al. 10.3390/rs16040627
8. Common hippopotamus in Nigeria: New census data and literature review confirm the conservation importance of sites outside protected areas L. Baker et al. 10.1002/aqc.3397
9. Comparing unmanned aerial systems with conventional methodology for surveying a wild white-tailed deer population M. McMahon et al. 10.1071/WR20204
10. Automated detection of wildlife using drones: Synthesis, opportunities and constraints E. Corcoran et al. 10.1111/2041-210X.13581
11. Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana V. Inman et al. 10.1371/journal.pone.0219652
12. Eye of the farmer in the sky: Drones S. GÜL et al. 10.51534/tiha.943842
13. Hidden Hippos: Using Photogrammetry and Multiple Imputation to Determine the Age, Sex, and Body Condition of an Animal Often Partially Submerged V. Inman & K. Leggett 10.3390/drones6120409
14. A Study on the Detection of Cattle in UAV Images Using Deep Learning J. Barbedo et al. 10.3390/s19245436
15. Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images S. Lee et al. 10.3390/rs13112169
16. The use of drones for studying the behaviour of mammals E. Berezina et al. 10.31857/S0044513424040117
17. Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation J. Virtue et al. 10.3390/drones7110674
18. Successful observation of orangutans in the wild with thermal-equipped drones C. Burke et al. 10.1139/juvs-2018-0035
19. Automatic Recognition of Black-Necked Swan (Cygnus melancoryphus) from Drone Imagery M. Jiménez-Torres et al. 10.3390/drones7020071
20. Drones for Conservation in Protected Areas: Present and Future J. Jiménez López & M. Mulero-Pázmány 10.3390/drones3010010
21. An experimental approach to evaluate the potential of drones in terrestrial mammal research: a gregarious ungulate as a study model N. Schroeder et al. 10.1098/rsos.191482
22. Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras C. Burke et al. 10.1080/01431161.2018.1558372
23. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau J. Peng et al. 10.1016/j.isprsjprs.2020.08.026
24. Drone Nest Searching Applications Using a Thermal Camera R. Stander et al. 10.1002/wsb.1211
25. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results J. Witczuk et al. 10.1080/01431161.2017.1390621
26. Evaluating new technology for biodiversity monitoring: Are drone surveys biased? E. Corcoran et al. 10.1002/ece3.7518
27. Effectiveness of using drones and convolutional neural networks to monitor aquatic megafauna A. Woolcock et al. 10.1111/aje.12950
28. The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions A. Rees et al. 10.3354/esr00877
29. Evaluation of low‐cost consumer‐grade UAVs for conducting comprehensive high‐frequency population censuses of hippopotamus populations C. Fritsch & C. Downs 10.1111/csp2.281
30. The direct and indirect effects of damming on the Hippopotamus amphibius population abundance and distribution at Bui National Park, Ghana G. Bempah et al. 10.3897/natureconservation.50.87411
31. Recounting bias can affect abundance estimates from intensive helicopter surveys of feral goats J. Tracey et al. 10.1071/WR22097
32. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems S. Longmore et al. 10.1080/01431161.2017.1280639
33. Perspectives on the use of unmanned aerial systems to monitor cattle J. Barbedo & L. Koenigkan 10.1177/0030727018781876
34. A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems C. Bird et al. 10.3390/rs12223692
35. Revue des applications et de l’utilité des drones en conservation de la faune P. Charbonneau & J. Lemaître 10.7202/1080620ar
36. Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter J. Virtue et al. 10.3390/drones5040119
37. Cattle detection and counting in UAV images based on convolutional neural networks W. Shao et al. 10.1080/01431161.2019.1624858
38. Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks M. Moreni et al. 10.3390/geomatics1010004
39. Detection of two Arctic birds in Greenland and an endangered bird in Korea using RGB and thermal cameras with an unmanned aerial vehicle (UAV) W. Lee et al. 10.1371/journal.pone.0222088
40. Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles G. He et al. 10.1002/ajp.23676
41. Automated cattle counting using Mask R-CNN in quadcopter vision system B. Xu et al. 10.1016/j.compag.2020.105300