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The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws.<\/jats:p>","DOI":"10.3233\/jcm-247185","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T12:17:06Z","timestamp":1718713026000},"page":"1523-1538","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent identification system of wild animals image based on deep learning in biodiversity conservation law"],"prefix":"10.66113","volume":"24","author":[{"given":"Xiaolong","family":"Liang","sequence":"first","affiliation":[{"name":"College of Intellectual Property, Hubei University of Automotive Technology, Shiyan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derun","family":"Pan","sequence":"additional","affiliation":[{"name":"College of 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