{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:27:26Z","timestamp":1791664046217,"version":"4.3.4"},"reference-count":37,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M771716"],"award-info":[{"award-number":["2025M771716"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4702501"],"award-info":[{"award-number":["2022YFB4702501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ecology and Evolution"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Monitoring wildlife in remote areas is vital for biodiversity research, yet infrared\u2010triggered cameras often produce large numbers of false or empty images due to environmental interference, causing data overload and power waste. To address this, this manuscript proposes WS\u2010YOLO, a lightweight edge\u2010optimized wildlife detection model based on improved YOLOv11n, which aims to integrate false\u2010trigger filtering and accurate animal detection in a single model for field applications. Specifically, the model introduces three key enhancements: it adopts a Wavelet Convolution (WTConv) module to capture multi\u2010scale and frequency\u2010aware features, boosting robustness to complex backgrounds and small\u2010target detection accuracy; meanwhile, a Spatial\u2010Channel Synergistic Attention (SCSA) mechanism is incorporated to enhance spatial localization and channel\u2010wise semantic encoding, enabling the model to focus on salient regions of wildlife; additionally, a SlideLoss function is designed to prioritize training on boundary cases, improving the handling of ambiguous detection instances. Experiments on four wildlife datasets demonstrate that WS\u2010YOLO achieves excellent mAP@0.50 performance: 93.0% on African Wildlife, 98.0% on Amur Tiger, 98.5% on the NTLNP infrared dataset, and 79.4% on PASCAL VOC, confirming its robustness across diverse scenarios. Deployment on a Jetson Nano further validates its feasibility for real\u2010time, energy\u2010efficient use in the field. WS\u2010YOLO enables accurate animal recognition and classification directly at edge devices, significantly reducing communication load and enhancing monitoring efficiency.<\/jats:p>","DOI":"10.1002\/ece3.73927","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T04:51:23Z","timestamp":1782708683000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Toward Precision Biodiversity Detection: An Edge\u2010Deployable Framework for Mitigating Data Redundancy"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5373-3879","authenticated-orcid":false,"given":"Huihui","family":"Sun","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering Huainan Normal University  Huainan China"},{"name":"School of Technology Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Technology Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering Huainan Normal University  Huainan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0376892919000298"},{"key":"e_1_2_10_3_1","unstructured":"Bochkovskiy A. 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B.Cho andY.Xiao.\u201cBalancing Latency and Accuracy on Deep Video Analytics at the Edge.\u201dIn 19th Annual Consumer Communications & Networking Conference (CCNC) Las Vegas NV USA 2022\u20101\u201012022; pp. 299\u2013306.","DOI":"10.1109\/CCNC49033.2022.9700636"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2024.124368"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2024.1513740"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"Liu W. D.Anguelov D.Erhan et\u00a0al.\u201cSSD: Single Shot MultiBox Detector.\u201dIn 14th European Conference on Computer Vision Cham 2016; pp. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9112276"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","unstructured":"Liu Z. Y.Lin Y.Cao et\u00a0al.\u201cSwin Transformer: Hierarchical Vision Transformer Using Shifted Windows.\u201dIn 2021 IEEE\/CVF International Conference on Computer Vision (ICCV) 2021\u20101\u201012021; pp. 9992\u201310002.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010023\u201015246\u20108"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2025.113227"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0110832"},{"key":"e_1_2_10_18_1","unstructured":"Megvii\u2010Basedetection.2025.\u201cYOLOX: Exceeding YOLO Series in 2021.\u201dhttps:\/\/github.com\/Megvii\u2010BaseDetection\/YOLOX."},{"key":"e_1_2_10_19_1","unstructured":"Meituan.2025.\u201cYOLOv6: A Single\u2010Stage Object Detection Framework Dedicated to Industrial Applications.\u201dhttps:\/\/github.com\/meituan\/YOLOv6\/tree\/main."},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13280\u2010015\u20100713\u20101"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1719367115"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj\u2010cs.1502"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/ecs2.70220"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"Redmon J. 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