Numerous animals are hurt day by day due to poaching, accidents, falls and fights. These animals face severe damage due to which their life becomes at risk. Some of them need immediate help for their injuries or they might die. The proposed system aims to save as much as injured animals by using a surveillance drone which will detect wounded animals using CNN model. The drone after detection alerts the wildlife authorities immediately with the location of the spotted injured animal and authorities can reach the spot as soon as possible to treat the wounded animal. By this way, the delay can be decreased and death due to late treatment can be avoided. Thereby, decreasing the count of deaths per day. Additionally, this can also help to preserve endangered animals which are at verge of extinction since injuries are one of the major factors which lead to their death and eventually to their extinction

Topics
Diseases and conditions
1.
Banupriya
,
N.
,
Saranya
,
S.
,
Swaminathan
,
R.
,
Harikumar
,
S.
, &
Palanisamy
,
S.
 (
2020
).
Animal detection using deep learning algorithm
.
Journal of Critical Reviews
,
7
(
1
),
434
–
439
.
Google Scholar
 
2.
Nguyen
,
H.
,
Maclagan
,
S. J.
,
Nguyen
,
T. D.
,
Nguyen
,
T.
,
Flemons
,
P.
,
Andrews
,
K.
, &
Phung
,
D.
 (
2017
, October).
Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring
.
In 2017 IEEE international conference on data science and advanced Analytics (DSAA
) (pp.
40
–
49
).
IEEE
.
Google Scholar
Crossref
Search ADS
 
3.
G.
Chen
,
T. X.
Han
,
Z.
He
,
R.
Kays
 and
T.
Forrester
, “
Deep convolutional neural network based species recognition for wild animal monitoring
,”
2014 IEEE International Conference on Image Processing (ICIP
),
2014
, pp.
858
–
862
,
https://doi.org/10.1109/ICIP.2014.7025172
.
Google Scholar
Crossref
Search ADS
 
4.
S. U.
Sharma
 and
D. J.
Shah
, “
A Practical Animal Detection and Collision Avoidance System Using Computer Vision Technique
,” in
IEEE Access
, vol.
5
, pp.
347
–
358
,
2017
,
https://doi.org/10.1109/ACCESS.2016.2642981
.
Google Scholar
Crossref
Search ADS
 
5.
Chalmers
,
C.
,
Fergus
,
P.
,
Curbelo
Montanez
, C. A.,
Longmore
,
S. N.
, &
Wich
,
S. A.
 (
2021
).
Video analysis for the detection of animals using convolutional neural networks and consumer-grade drones
.
Journal of Unmanned Vehicle Systems
,
9
(
2
),
112
–
127
.
https://doi.org/10.1139/juvs-2020-0018
Google Scholar
Crossref
Search ADS
 
6.
Kinaneva
,
D.
,
Hristov
,
G.
,
Raychev
,
J.
, &
Zahariev
,
P.
 (
2019
, May). Early forest fire detection using drones and artificial intelligence. In
2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
(pp.
1060
–
1065
).
IEEE
.
Google Scholar
Crossref
Search ADS
 
7.
Allauddin
,
M. S.
,
Kiran
,
G. S.
,
Kiran
,
G. R.
,
Srinivas
,
G.
,
Mouli
,
G. U. R.
, &
Prasad
,
P. V.
 (
2019
, July). Development of a surveillance system for forest fire detection and monitoring using drones. In
IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium
(pp.
9361
–
9363
).
IEEE
.
Google Scholar
Crossref
Search ADS
 
8.
Vayssade
,
J. A.
,
Arquet
,
R.
, &
Bonneau
,
M.
 (
2019
).
Automatic activity tracking of goats using drone camera
.
Computers and Electronics in Agriculture
, 162,
767
–
772
.
Google Scholar
 
9.
Sharma
,
N.
,
Scully-Power
,
P.
, &
Blumenstein
,
M.
 (
2018
, December). Shark detection from aerial imagery using region-based CNN, a study. In
Australasian Joint Conference on Artificial Intelligence
(pp.
224
–
236
).
Springer
,
Cham
.
Google Scholar
 
10.
Gray
,
P. C.
,
Fleishman
,
A. B.
,
Klein
,
D. J.
,
McKown
,
M. W.
,
Bezy
,
V. S.
,
Lohmann
,
K. J.
, &
Johnston
,
D. W.
 (
2019
).
A convolutional neural network for detecting sea turtles in drone imagery
.
Methods in Ecology and Evolution
,
10
(
3
),
345
–
355
.
https://doi.org/10.1111/2041-210X.13132
Google Scholar
Crossref
Search ADS
 
11.
Thakkar
,
Parth
 &
Balaji
,
Anand
 &
Narwane
,
Vaibhav
. (
2019
).
Intelligent Unmanned Aerial Vehicles: ICIMA
2018.
https://doi.org/10.1007/978-981-13-2490-1_44
.
Google Scholar
 
12.
Oh
S
,
Ham
S
,
Lee
S.
Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes
.
Energies
.
2021
;
14
(
19
):
6359
.
https://doi.org/10.3390/en14196359
Google Scholar
Crossref
Search ADS
 
13.
Safety and security management with Unmanned Aerial Vehicle (UAV) in oil and gas industry Jaeyoung Choa, Gino Lima,*, Taofeek Biobakua, Seonjin Kima, Hamid Parsaeib
.
14.
Lee
,
T.
;
Mckeever
,
S.
;
Courtney
,
J.
Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
.
Drones
2021
,
5
,
52
https://doi.org/10.3390/drones5020052
Google Scholar
Crossref
Search ADS
 
15.
Mehta
,
P. L.
,
Kalra
,
R.
, &
Prasad
,
R.
 (
2021
). A Backdrop Case Study of AI-Drones in Indian Demographic Characteristics Emphasizing the Role of AI in Global Cities Digitalization.
Wireless personal communications
,
1
–
21
.
Advance online publication
.
https://doi.org/10.1007/s11277-020-08014-6
Google Scholar
Crossref
Search ADS
 
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