Automatic detection for the world's rarest primates based on a tropical rainforest environment

Wang, Xiaolei; Wen, Su; Niu, Ning; Wang, Guanjun; Long, Wenxing; Zou, Yong; Huang, Mengxing

doi:10.1016/j.gecco.2022.e02250

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In order to verify the robustness of the proposed algorithm on different datasets, our team took a large number of IR and visible images of Hainan gibbons (Nomascus hainanus) using a drone [39]. Hainan gibbons are the most endangered primates in the world and are in danger of extinction at any time [40]. The Hainan gibbon is listed as critically endangered by the International Union for Conservation of Nature Red List of Threatened Species [41].…”

Section: Hainan Gibbon (Nomascus Hainanus) Datasetmentioning

confidence: 99%

SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

Li,

Wang,

Zhang

et al. 2023

Remote Sensing

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Infrared and visible image fusion is a solution that generates an information-rich individual image with different modal information by fusing images obtained from various sensors. Salient detection can better emphasize the targets of concern. We propose a residual Swin Transformer fusion network based on saliency detection, termed SDRSwin, aiming to highlight the salient thermal targets in the infrared image while maintaining the texture details in the visible image. The SDRSwin network is trained with a two-stage training approach. In the first stage, we train an encoder–decoder network based on residual Swin Transformers to achieve powerful feature extraction and reconstruction capabilities. In the second stage, we develop a novel salient loss function to guide the network to fuse the salient targets in the infrared image and the background detail regions in the visible image. The extensive results indicate that our method has abundant texture details with clear bright infrared targets and achieves a better performance than the twenty-one state-of-the-art methods in both subjective and objective evaluation.

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Section: Hainan Gibbon (Nomascus Hainanus) Datasetmentioning

confidence: 99%

SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

Li,

Wang,

Zhang

et al. 2023

Remote Sensing

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“…Habitat transformation, natural forest cover, landscape shape index, and distance to the nearest roads-Zhang et al [3] pointed out that these four factors led to a dramatic decline of Hainan gibbons. There are only 37 remaining Hainan gibbons in the world, living in approximately 15 square kilometers of forest fragments in the Bawangling National Nature Reserve in Hainan province, China [4]. The Hainan gibbon is an "extremely endangered" species and is in danger of extinction.…”

Section: Introductionmentioning

confidence: 99%

“…Chan et al [7] built several artificial canopy bridges over the forest for the Hainan gibbons to pass through. Wang et al [4] used an infrared-triggered camera to recognize Hainan gibbons, but the camera was only able to capture a fixed, small range of images.…”

Section: Introductionmentioning

confidence: 99%

Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

Li,

Wang,

Zhang

et al. 2023

Drones

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The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world. Infrared and visible images taken by drones are an important and effective way to observe Hainan gibbons. However, a single infrared or visible image cannot simultaneously observe the movement tracks of Hainan gibbons and the appearance of the rainforest. The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. We propose a fusion method of infrared and visible images of the Hainan gibbon for the first time, termed Swin-UetFuse. The Swin-UetFuse has a powerful global and long-range semantic information extraction capability, which is very suitable for application in complex tropical rainforest environments. Firstly, the hierarchical Swin Transformer is applied as the encoder to extract the features of different scales of infrared and visible images. Secondly, the features of different scales are fused through the l1-norm strategy. Finally, the Swing Transformer blocks and patch-expanding layers are utilized as the decoder to up-sample the fusion features to obtain the fused image. We used 21 pairs of Hainan gibbon datasets to perform experiments, and the experimental results demonstrate that the proposed method achieves excellent fusion performance. The infrared and visible image fusion technology of drones provides an important reference for the observation and protection of the Hainan gibbons.

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An integrated passive acoustic monitoring and deep learning pipeline for black‐and‐white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

Batist,

Dufourq,

Jeantet

et al. 2024

American J Primatol

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The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black‐and‐white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar‐shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in‐person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May–July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57‐h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in‐person observations, saving time, money, and labor while also providing re‐analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open‐sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

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Automatic detection for the world's rarest primates based on a tropical rainforest environment

Cited by 4 publications

References 20 publications

SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

An integrated passive acoustic monitoring and deep learning pipeline for black‐and‐white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

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