Animal Detection in Highly Cluttered Natural Scenes by using Faster R-CNN
Wenjun Yu1, Sumi Kim2, Jeong-Hyu Lee3, Jaeho Choi4

1Wenjun Yu, Department of Electronic Engineering, CAIIT, Chonbuk National University, Chonju, Korea.
2Sumi Kim, Seoyeong University, Gwangju, Korea.
3Jeong-Hyu Lee, His Department Name, University, College, Organization Name, Chonju, Korea.
4Jaeho Choi, Department of SW Engineering, Chonbuk National University, Chonju, Korea.
Manuscript received on 21 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1311-1313 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10590882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1059.0882S819
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Abstract: With the increasing awareness of environmental protection, people are paying more and more attention to the protection of wild animals. Their survive-al is closely related to human beings. As progress in target detection has achieved unprecedented success in computer vision, we can more easily tar-get animals. Animal detection based on computer vision is an important branch of object recognition, which is applied to intelligent monitoring, smart driving, and environmental protection. At present, many animal detection methods have been proposed. However, animal detection is still a challenge due to the complexity of the background, the diversity of animal pos-es, and the obstruction of objects. An accurate algorithm is needed. In this paper, the fast Region-based Convolutional Neural Network (Faster R-CNN) is used. The proposed method was tested using the CAMERA_TRAP DATASET. The results show that the proposed animal detection method based on Faster R-CNN performs better in terms of detection accuracy when its performance is compared to conventional schemes.
Keywords: Deep Learning, Convolutional Neural Networks, Faster R-Cnn, Animal Recogni-Tion, RPN.
Scope of the Article: Natural Language Processing