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Animals
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Sensors-Assisted Observation of Wildlife
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Sensors-Assisted Observation of Wildlife

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Wildlife".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4584

Special Issue Editors

Dr. Meredith S. Palmer

E-Mail Website
Guest Editor
Fauna & Flora International, Washington, DC, USA
Interests: conservation technology; predator–prey interactions; landscapes of fear
Talia Speaker

E-Mail Website
Guest Editor Assistant
1. World Wildlife Fund (WWF), Washington, DC, USA
2. WILDLABS, Washington, DC, USA
Interests: conservation technology; new developments in technological applications for wildlife monitoring; biodiversity conservation

Special Issue Information

Dear Colleagues,

As threats to species and ecosystems accelerate, the development and implementation of technology for the monitoring, protection, and conservation of biodiversity has become increasingly important. Conventional methods to observe and quantitatively monitor wildlife can be limited in scope and logistically challenging; only in recent years have developments in remote, sensor-assisted wildlife monitoring enabled us to start mitigating these limitations. These cutting-edge approaches, such as advanced biologgers, accelerometers, satellite tags, and remote cameras, are revolutionizing the field of biology and greatly advancing global conservation efforts. For this Special Issue, we invite articles on the application of sensor technology in wild animal studies, including individual detection, population surveys, and behavioural and physiological studies, as well as the development of novel sensor-based conservation solutions. Additionally, we welcome submissions that investigate the limitations and obstacles to the widespread adoption of these tools. Through a collection of research articles, reviews, perspectives, and summary opinions, this Special Issue aims to showcase the full diversity of this field, highlighting current and future trends in basic and applied research.

Dr. Meredith S. Palmer
Guest Editor
Talia Speaker
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • conservation technology
  • wildlife observation
  • movement ecology
  • biodiversity conservation
  • animal tracking
  • telemetry
  • remote sensing
  • bioacoustics
  • camera traps
  • technology
  • innovation
  • sensors
  • sensor network
  • Internet of Things
  • drones
  • animal behaviour
  • biotelemetry
  • passive acoustic monitoring
  • behavioural ecology

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Published Papers (2 papers)

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Research

20 pages, 6428 KiB  
Article
Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning
by Zhaojin Guo, Zheng He, Li Lyu, Axiu Mao, Endai Huang and Kai Liu
Animals 2024, 14(1), 159; https://doi.org/10.3390/ani14010159 - 3 Jan 2024
Cited by 1 | Viewed by 1721
Abstract
The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population [...] Read more.
The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population in the concerned regions, which, however, is very difficult in urban environments due to the concealment and mobility of pigeons within complex building structures. With the advances in deep learning, computer vision can be a promising tool for pigeon monitoring and population estimation but has not been well investigated so far. Therefore, we propose an improved deep learning model (Swin-Mask R-CNN with SAHI) for feral pigeon detection. Our model consists of three parts. Firstly, the Swin Transformer network (STN) extracts deep feature information. Secondly, the Feature Pyramid Network (FPN) fuses multi-scale features to learn at different scales. Lastly, the model’s three head branches are responsible for classification, best bounding box prediction, and segmentation. During the prediction phase, we utilize a Slicing-Aided Hyper Inference (SAHI) tool to focus on the feature information of small feral pigeon targets. Experiments were conducted on a feral pigeon dataset to evaluate model performance. The results reveal that our model achieves excellent recognition performance for feral pigeons. Full article
(This article belongs to the Special Issue Sensors-Assisted Observation of Wildlife)
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19 pages, 7948 KiB  
Article
A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s
by Wenhan Yang, Tianyu Liu, Ping Jiang, Aolin Qi, Lexing Deng, Zelong Liu and Yuchen He
Animals 2023, 13(19), 3134; https://doi.org/10.3390/ani13193134 - 7 Oct 2023
Cited by 7 | Viewed by 2321
Abstract
A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to [...] Read more.
A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to which data augmentation and expansion methods are applied to extensively train the proposed model. To enhance the feature extraction ability of the proposed model, a weighted channel stitching method based on channel attention is introduced. The Swin Transformer module is combined with a CNN network to add a Self-Attention mechanism, thus improving the perceptual field for feature extraction. Furthermore, a new loss function (DIOU_Loss) and an adaptive class suppression loss (L_BCE) are adopted to accelerate the model’s convergence speed, reduce false detections in confusing categories, and increase its accuracy. When comparing our improved algorithm with the original YOLOv5s network model under the same experimental conditions and data set, significant improvements are observed, in particular, the mean average precision (mAP) is increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Our improved algorithm also outperforms popular target detection algorithms, including YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN. Our proposed improvement measures can well address the challenges posed by the low contrast between background and targets, as well as occlusion and overlap, in forest wildlife images captured by trap cameras. These measures provide practical solutions for enhanced forest wildlife protection and facilitate efficient data acquisition. Full article
(This article belongs to the Special Issue Sensors-Assisted Observation of Wildlife)
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