Abstract
This paper presents a system for automatic detection and recognition of the animals using Deep CNN with genetic segmentation. In the present work, the grouping of input animal pictures is done with the help of a Convolutional Neural Network is demonstrated. The proposed work is compared with standard recognition methods such as SU, DS, MDF, LEGS, DRFI, MR, GC. The existing methodologies have more error rates because of high false-positive & negative rate detection, hence there is a need for a highly accurate system for animal detection. According to the proposed work, a genetic algorithm is used for the segmentation process, and for classification 3-layers neural network is used. For training and examining the proposed work, a database is created which consists of 100 distinct subjects with 2 classes and 10 pictures in each class. Experimental results are demonstrated as the segmentation using genetic algorithms and the novelty of the proposed method in terms of precision, recall, f-measurement, and MAE. Hence proposed work improves the overall results i.e. precision (99.02%), recall (98.79%), F-Measurement (98.9%), and MAE (0.78%).
Similar content being viewed by others
References
Angayarkkani K, Radhakrishnan N (2011). An effective technique to detect forest fire region through ANFIS with spatial data. In 2011 3rd International Conference on Electronics Computer Technology (Vol. 3, pp. 24–30). IEEE
Banupriya N, Saranya S, Swaminathan R, Harikumar S, Palanisamy S (2020) Animal detection using deep learning algorithm. J Crit Rev. https://doi.org/10.31838/jcr.07.01.85 (ISSN- 2394-5125)
Bryson M, Reid A, Ramos F, Sukkarieh S (2010) Airborne vision-based mapping and classification of large farmland environments. J Field Robot 27(5):632–655
Casbeer DW, Kingston DB, Beard RW, McLain TW (2006) Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int J Syst Sci 37(6):351–360
Chandrakar R, Raja R, Miri R, Tandan SR (2020) Vehicle detection on sanctuaries using spatially distributed convolutional neural network. SAMRIDDHI J Phys Sci Eng Technol 12(3):116–121
Chandrakar R, Raja R, Miri R, Tandan SR, Laxmi KR (2020) Detection and identification of animals in wild life sancturies using convolutional neural network. Int J Recent Technol Eng (IJRTE). https://doi.org/10.35940/ijrte.E4579.018520
Cheng MM, Warrell J, Lin WY, Zheng S, Vineet V, Crook N (2013) Efficient salient region detection with soft image abstraction, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1529–1536
Eisenbeiss H, Zhang L (2006) Comparison of DSMs generated from mini UAV imagery and terrestrial laser scanner in a cultural heritage application. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):90–96
Grenzdörffer GJ, Engel A, Teichert B (2008) The photogrammetric potential of low-cost UAVs in forestry and agriculture. Int Arch Photogramm Remote Sens Spat Inf Sci 31(B3):1207–1214
Hung C, Bryson M, Sukkarieh S (2012) Multi-class predictive template for tree crown detection. ISPRS J Photogramm Remote Sens 68:170–183
Hung C, Xu Z, Sukkarieh S (2014) Feature learning-based approach for weed classification using high-resolution aerial images from a digital camera mounted on a UAV. Remote Sens 6(12):12037–12054
Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: A discriminative regional feature integration approach, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090.
Kruthiventi SS, Gudisa V, Dholakiya JH, Babu RV (2016) Saliency unified: a deep architecture for simultaneous eye fixation prediction and salient object segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 5781–5790.
Kumar A, Sharaff A (2021) Performance enhancement of gene mention tagging by using deep learning and biomedical named entity recognition. Intelligent Data Engineering and Analytics. Springer, Singapore, pp 637–645
Lambers K, Eisenbeiss H, Sauerbier M, Kupferschmidt D, Gaisecker T, Sotoodeh S, Hanusch T (2007) Combining photogrammetry and laser scanning for the recording and modeling of the late intermediate period site of Pinchango Alto, Palpa, Peru. J Archaeol Sci 34(10):1702–1712
Li G, Yu Y (2015) Visual saliency based on multiscale deep features, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455–5463.
Liu N, Han J (2016) DHSNet: Deep hierarchical saliency network for salient object detection, in: IEEE Conference on Computer Vision and Pattern Recognition. pp. 678–686
Luis J, Galán J, Espigado J (2015) Low power wireless smoke alarm system in home fires. Sensors 15(8):20717–20729
Mathew J (2015) Vertical edge detection for car license plate recognition. DJ J Adv Electron Commun Eng 1(1):8–15
Medikonda VR, Janarthanan V (2017) Identifying image falsification by enhanced auto colour correlation approach—a forgery forensic. DJ J Adv Electron Commun Eng 3(2):1–10
Premal CE, Vinsley SS (2014) Image processing based forest fire detection using YCbCr color model. In 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] (pp. 1229–1237). IEEE.
Raja R, Kumar S, Mahmood MR (2020) Color object detection based image retrieval using roi segmentation with multi-feature method. Wireless Pers Commun. https://doi.org/10.1007/s11277-019-07021-6 (Print ISSN0929-6212 online ISSN1572-834)
Rawat N, Raja R (2016) Moving vehicle detection and tracking using modified mean shift method and kalman filter and research. Int J New Technol Res (IJNTR) 2(5):96–100 (ISSN: 2454-4116)
Sardouk A, Mansouri M, Merghem-Boulahia L, Gaiti D, Rahim-Amoud R (2013) Crisis management using MAS-based wireless sensor networks. Comput Netw 57(1):29–45
Sauerbier M, Eisenbeiss H (2010) UAVs for the documentation of archaeological excavations. Int Arch Photogramm Remote Sens Spat Inf Sci 38(5):526–531
Saxena A, Gupta DK, Singh S (2021) An animal detection and collision avoidance system using deep learning. In: Hura G, Singh A, Siong Hoe L (eds) Advances in communication and computational technology. Lecture notes in electrical engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_81
Sharaff A, Khurana S, Cheepurupalli K, Sahu T (2020) Personalized Recommendation System with User Interaction based on LMF and Popularity Model. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1–6). IEEE.
Sibanda V, Mpofu K, Trimble J, Zengeni N (2019) Design of an animal detection system for motor vehicle drivers. Procedia CIRP 84:755–760. https://doi.org/10.1016/j.procir.2019.04.175 (ISSN 2212-8271)
Spiess T, Bange J, Buschmann M, Vörsmann P (2007) First application of the meteorological Mini-UAV‘M2AV.’ Meteorol Z 16(2):159–169
Turner D, Lucieer A, Malenovský Z, King D, Robinson S (2014) Spatial co-registration of ultra-high-resolution visible, multispectral and thermal images acquired with a micro-UAV over Antarctic moss beds. Remote Sens 6(5):4003–4024
Ulucinar AR, Korpeoglu I, Cetin AE (2014) A Wi-Fi cluster-based wireless sensor network application and deployment for wildfire detection. Int J Distrib Sensor Netw 10(10):651957
Wang L, Wang L, Lu H, Zhang P, Xiang R (2018) Saliency detection with recurrent fully convolutional networks. European Conference on computer vision. Springer, Cham, pp 825–841
Yan X, Cheng H, Zhao Y, Yu W, Huang H, Zheng X (2016) Real-time identification of smoldering and flaming combustion phases in forest using a wireless sensor network-based multi-sensor system and artificial neural network. Sensors 16(8):1228
Zhang Z, He Z, Cao G, Cao W (2016) Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification”. IEEE Trans Multimedia 18(10):2079–2092
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval
This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.
Conflict of interest
The authors declare that there is no conflict of interest related to this paper.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chandrakar, R., Raja, R. & Miri, R. Animal detection based on deep convolutional neural networks with genetic segmentation. Multimed Tools Appl 81, 42149–42162 (2022). https://doi.org/10.1007/s11042-021-11290-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11290-4