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Animals
Special Issues
Recent Advances and Innovation in Wildlife Population Estimation
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Recent Advances and Innovation in Wildlife Population Estimation

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

Deadline for manuscript submissions: closed (1 September 2024) | Viewed by 2934

Special Issue Editor

Prof. Dr. Dimitrios Bakaloudis

E-Mail Website
Guest Editor
School of Forestry and Natural Environment, Laboratory of Wildlife and Freshwater Fisheries, Aristotle University of Thessaloniki, Thessaloniki, Greecee
Interests: wildlife conservation; wildlife ecology; biodiversity monitoring; behavioral ecology; animal ecology; invasive species ecology and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The estimation of population size is fundamental to wildlife management and conservation. Recently, high-tech devices have been used more frequently to monitor wild animals in an effort uncover behaviors that have until now been mysteries, but also to accurately assess biodiversity in remote areas.

The Special Issue aims to provide a forum for collating innovative techniques on wildlife population estimation. We welcome original research or review articles which focus on technology including (but not limited to) innovative wildlife monitoring techniques, such as camera traps, thermal cameras, implanting devices, satellite remote sensing, drones, environmental DNA (eDNA), acoustic sensors, etc. for use to conserve wildlife populations. In addition, papers from a wide range of disciplines, such as citizen science, artificial intelligence, deep neural networks, and machine learning are also welcome.

As this is a new and emerging research area, the knowledge on these topics will shed light on the most promising techniques in the realm of wildlife conservation going forward.

Prof. Dr. Dimitrios Bakaloudis
Guest Editor

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

  • wildlife technology
  • monitoring wildlife
  • new technology in wildlife conservation
  • wildlife ecology
  • wildlife surveys

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Published Papers (2 papers)

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Research

25 pages, 51247 KiB  
Article
CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model
by Feng Yang, Chunying Hu, Aokang Liang, Sheng Wang, Yun Su and Fu Xu
Animals 2024, 14(19), 2909; https://doi.org/10.3390/ani14192909 - 9 Oct 2024
Viewed by 904
Abstract
Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed [...] Read more.
Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. Full article
(This article belongs to the Special Issue Recent Advances and Innovation in Wildlife Population Estimation)
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22 pages, 10343 KiB  
Article
Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China
by Wenjie Mao, Gang Li and Xiaowei Li
Animals 2024, 14(8), 1152; https://doi.org/10.3390/ani14081152 - 10 Apr 2024
Cited by 2 | Viewed by 1012
Abstract
To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object [...] Read more.
To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications. Full article
(This article belongs to the Special Issue Recent Advances and Innovation in Wildlife Population Estimation)
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