Postural behavior recognition of captive nocturnal animals based on deep learning: a case study of Bengal slow loris

Yi, Lei; Dong, Pengmei; Guan, Yan; Xiang, Ying; Xie, Meng; Mu, Jiong; Wang, Yongzhao; Ni, Qingyong

doi:10.1038/s41598-022-11842-0

Cited by 4 publications

(2 citation statements)

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“…In [9], YOLOv5 eliminates duplicate and similar images. In [10], by employing computer vision tools, a picture processing system was created and built to enhance contrast in pictures and segment pertinent image subdivisions. The photos were revised to expedite the procedure.…”

Section: B Pre-processingmentioning

confidence: 99%

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A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

Kumar,

Luo,

Shaukat

2023

IJACSA

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Integrating deep learning techniques into computer vision application has ushered in a new era of automated analysis and interpretation of visual data. In recent years, a surge of interest has been witnessed in applying these methodologies towards detecting animals in video streams, promising transformative impacts on diverse fields such as ecology and agriculture. This paper presents an extensive and meticulous review of the latest deep-learning approaches employed for animal detection in video data. This study looks closely at ways to detect animals in videos using deep learning. This study explores various Deep learning methods for detecting many animals in multiple environments. The analysis also pays close attention to preparing the data, picking out important features, and reusing what has been learned from one task to help with another. In addition to highlighting successful methodologies, this review addresses the challenges and limitations inherent in these approaches issues such as limited data availability and adapting to technological advancements present significant hurdles. Recognising and understanding these challenges is crucial in shaping the future focus of research endeavours. Thus, this comprehensive review is an indispensable tool for anyone keen on employing these potent computer methods for animal detection in videos. It takes the latest ideas and shows where study can explore further to improve them. Furthermore, this comprehensive review has demonstrated that a more sustainable and balanced relationship between humans and animals can be achieved by harnessing the power of deep learning in animal detection. This research contributes to computer vision and holds immense promise in safeguarding biodiversity and promoting responsible land use practices, especially within agricultural domains. The insights from this study propel us towards a future where advanced technology and ecological harmony go hand in hand, ultimately benefiting both humans and the animal kingdom. The survey aims to provide a comprehensive overview of the cutting-edge developments in applying deep learning models for animal detection through cameras by elucidating the significance of these techniques in advancing the accuracy and efficiency of animal detection processes.

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Section: B Pre-processingmentioning

confidence: 99%

“…Struggles to detect small objects. Real-time monitoring of activities High computational requirements [10,2022] Deep learning technology Accuracy of 95.1%…”

Section: It Attained 87% Accuracymentioning

confidence: 99%