<i>Panthera unica</i> recognition based on data expansion and ResNeSt with few samples

Zhang Yu; Gao Yayue; Chang Fengyuan; Xie Jiangjian; Zhang Junguo

doi:10.12171/j.1000-1522.20210185

Journal of Beijing Forestry University > 2021 > 43(10): 89-99. > DOI: 10.12171/j.1000-1522.20210185

Zhang Yu, Gao Yayue, Chang Fengyuan, Xie Jiangjian, Zhang Junguo. Panthera unica recognition based on data expansion and ResNeSt with few samples[J]. Journal of Beijing Forestry University, 2021, 43(10): 89-99. DOI: 10.12171/j.1000-1522.20210185

Citation:

PDF (18312 KB)

Panthera unica recognition based on data expansion and ResNeSt with few samples

1.
Qinghai Qilian Mountain Nature Reserve Administration, Qilian County, 810400, Qinghai, China
2.
School of Technology, Beijing Forestry University, Beijing 100083, China

More Information

Received Date: May 13, 2021
Revised Date: June 08, 2021
Available Online: August 03, 2021
Published Date: October 29, 2021

Graphical Abstract

Abstract

Abstract

Objective The quality of snow leopard monitoring images collected by infrared trigger cameras is uneven and the number is limited. An automatic recognition method of snow leopard monitoring images based on deep learning data expansion was proposed to improve the recognition accuracy of the snow leopard under limited samples.
Method Improving the ResNeSt50 model with attention mechanism, the snow leopard monitoring images of Qilian Mountain National Park of northwestern China were used as the original dataset, the non-snow leopard terrestrial wildlife images taken by the infrared trigger camera were used as the extended negative sample, and the network snow leopard images were used as the extended positive sample. Comparative experiments were conducted in turn based on the above three datasets. The model was gradually guided to focus on the key characteristics of individual snow leopards by choosing an appropriate expansion method, and the effectiveness of the data expansion was verified by Gradient-weighted Class Activation Map.
Result The model trained with the original data set+expanded negative samples+expanded positive samples had the best recognition effect. The Grad-CAM showed that the model correctly focused on the individual pattern and spot characteristics of the snow leopard. Compared with the recognition model based on Vgg16 and ResNet50, ResNeSt50 achieved the best recognition effect, the test set recognition accuracy rate reached 97.70%, the precision rate reached 97.26%, and the recall rate reached 97.59%.
Conclusion The model trained by the original data set+extended negative sample+extended positive sample data expansion method proposed in this paper can distinguish the background from the foreground, and has a strong ability to discriminate the characteristics of snow leopard itself, and the generalization ability is the best.
- Panthera unica,
- monitoring image,
- few sample,
- data expansion,
- convolutional neural network

FullText(HTML)

References (24)

References

[1]	洪洋, 张晋东, 王玉君. 雪豹生态与保护研究现状探讨[J]. 四川动物, 2020, 39(6):711−720. doi: 10.11984/j.issn.1000-7083.20190438 Hong Y, Zhang J D, Wang Y J. Progress in the ecology and conservation research on Panthera unica[J]. Sichuan Journal of Zoology, 2020, 39(6): 711−720. doi: 10.11984/j.issn.1000-7083.20190438
[2]	Bracciale L, Catini A, Gentile G, et al. Delay tolerant wireless sensor network for animal monitoring: the pink iguana case[C]//Alessandro D G. Proceedings of International Conference on Applications in Electronics Pervading Industry, Environment and Society. Cham, Switzerland: Springer , 2016: 18−26.
[3]	徐峰. 新疆雪豹研究简史[J]. 人与生物圈, 2020(增刊 1):77−79. Xu F. A brief history of snow leopard in Xinjiang[J]. Man & Biosphere, 2020(Suppl. 1): 77−79.
[4]	马鸣, 徐峰, 吴逸群, 等. 新疆雪豹种群密度监测方法探讨[J]. 生态与农村环境学报, 2011, 27(1):79−83. doi: 10.3969/j.issn.1673-4831.2011.01.016 Ma M, Xu F, Wu Y Q, et al. Monitoring of population density of snow leopard in Xinjiang[J]. Journal of Ecology and Rural Environment, 2011, 27(1): 79−83. doi: 10.3969/j.issn.1673-4831.2011.01.016
[5]	汪六三, 黄子良, 王儒敬. 基于近红外光谱和机器学习的大豆种皮裂纹识别研究[J]. 农业机械学报, 2021, 52(6):361−368. doi: 10.6041/j.issn.1000-1298.2021.06.038 Wang L S, Huang Z L, Wang R J. Identification of soybean seed coat crack using near infrared spectroscopy and machine learning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(6): 361−368. doi: 10.6041/j.issn.1000-1298.2021.06.038
[6]	杨晓花, 高海云. 基于改进贝叶斯的书目自动分类算法[J]. 计算机科学, 2018, 45(8):203−207. Yang X H, Gao H Y. Improved bayesian algorithm based automatic classification method for bibliography[J]. Computer Science, 2018, 45(8): 203−207.
[7]	Majdar R S, Ghassemian H. A probabilistic svm approach for hyperspectral image classification using spectral and texture features[J]. International Journal of Remote Sensing, 2017, 38(15): 4265−4284. doi: 10.1080/01431161.2017.1317941
[8]	Le Cun Y, Boser B, Denker J S, et al. Handwritten digit recognition with a back-propagation network[C]// Touretzky D S. Advances in neural information processing systems. San Francisco: Morgan Kaufmann, 1990: 396−404.
[9]	Le Cun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278−2324. doi: 10.1109/5.726791
[10]	Okafor E, Pawara P, Karaaba F, et al. Comparative study between deep learning and bag of visual words for wild-animal recognition[C]//2016 IEEE symposium series on computational intelligence (SSCI). Athens: IEEE, 2017: 1−9.
[11]	向秋敏. 野生动物监测图像显著性检测算法及应用[D]. 北京: 北京林业大学, 2017. Xiang Q M. Saliency detection and application in wildlife monitoring images[D]. Beijing: Beijing Forestry University, 2017.
[12]	Horn G V, Aodha O M, Song Y, et al. The iNaturalist species classification and detection dataset[C]// Mortensen E. 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Salt Lake City: Utah, 2018(3): 132−139.
[13]	Timm M, Maji S, Fuller T. Large-scale ecological analyses of animals in the wild using computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition workshops. Salt Lake City: IEEE, 2018: 1896−1898.
[14]	王柯力, 袁红春. 基于迁移学习的水产动物图像识别方法[J]. 计算机应用, 2018, 38(5):1304−1308, 1326. Wang K L, Yuan H C. Aquatic animal image classification method based on transfer learning[J]. Journal of Computer Applications, 2018, 38(5): 1304−1308, 1326.
[15]	Willi M, Pitman R T, Cardoso A W, et al. Identifying animal species in camera trap images using deep learning and citizen science[J]. Methods in Ecology and Evolution, 2019, 10(1): 80−91. doi: 10.1111/2041-210X.13099
[16]	陈争涛, 黄灿, 杨波,等. 基于迁移学习的并行卷积神经网络牦牛脸识别算法[J]. 计算机应用, 2021, 41(5):1332−1336. Chen Z T, Huang C, Yang B, et al. Parallel convolutional neural network yak face recognition algorithm based on transfer learning[J]. Computer Application, 2021, 41(5): 1332−1336.
[17]	赵歆. 基于ResNet网络的奶山羊行为识别方法研究[D]. 西安: 西北农林科技大学, 2020. Zhao X. Research on dairy goat behavior recognition method based on resnet network[D]. Xi’an: Northwest A&F University, 2020.
[18]	程浙安. 基于深度卷积神经网络的内蒙古地区陆生野生动物自动识别[D]. 北京: 北京林业大学, 2019. Cheng Z A. Automatic recognition of terrestrial wildlife in Inner Mongolia based on deep convolutional neural network[D]. Beijing: Beijing Forestry University, 2019.
[19]	李安琪. 基于卷积神经网络的野生动物监测图像自动识别方法研究[D]. 北京: 北京林业大学, 2020. Li A Q. Research on automatic recognition method of wildlife monitoring images based on convolutional neural network[D]. Beijing: Beijing Forestry University, 2020.
[20]	Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]// 2017 IEEE conference on computer vision and pattern recognition (CVPR). Honolulu: IEEE, 2016: 5987−5995.
[21]	赵凯琳, 靳小龙, 王元卓. 小样本学习研究综述[J]. 软件学报, 2021, 32(2):349−369. Zhao K L, Jin X L, Wang Y Z. Survey on few-shot learning[J]. Journal of Software, 2021, 32(2): 349−369.
[22]	Royle J A, Dorazio R M, Link W A. Analysis of multinomial models with unknown index using data augmentation[J]. Journal of Computational and Graphical Statistics, 2007, 16(1): 67−85. doi: 10.1198/106186007X181425
[23]	Zhang H, Wu C, Zhang Z, et al. ResNeSt: split-attention networks[J/OL]. arXiv, 2020 [2021−05−25]. https://arxiv.org/abs/2004.08955.
[24]	Li X, Wang W, Hu X, et al. Selective kernel networks[C]// Brendel W. 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Long Beach: IEEE, 2019: 510−519.

[1]	Yang Dongye, Yu Xinxiao, Li Xuhong, Jiang Tao, Jia Guodong. Characteristics of sap flow of degraded Populus simonii in Bashang Area, Hebei Province of northern China and its response to environmental factors[J]. Journal of Beijing Forestry University, 2024, 46(7): 36-43. DOI: 10.12171/j.1000-1522.20230332
[2]	Zheng Dongsheng, Liu Qijing. Effects of environmental factors on forest community distribution in Changbai Mountain Nature Reserve of northeastern China[J]. Journal of Beijing Forestry University, 2023, 45(8): 57-64. DOI: 10.12171/j.1000-1522.20220086
[3]	Ma Jing, Guo Jianbin, Liu Zebin, Wang Yanhui, Zhang Ziyou. Diurnal variations of stand transpiration of Larix principis-rupprechtii forest and its response to environmental factors in Liupan Mountains of northwestern China[J]. Journal of Beijing Forestry University, 2020, 42(5): 1-11. DOI: 10.12171/j.1000-1522.20190468
[4]	LI Xin-yu, LI Yan-ming, SUN Lin, XU Rui, ZHAO Song-ting, GUO Jia. Characteristics of transpiration water consumption and its relationship with environmental factors in Ginkgo biloba[J]. Journal of Beijing Forestry University, 2014, 36(4): 23-29. DOI: 10.13332/j.cnki.jbfu.2014.04.008
[5]	CHI Bo, CAI Ti-jiu, MAN Xiu-ling, LI Yi.. Effects of influencing factors on stem sap flow in Larix gmelinii in northern Da Hinggan Mountains, northeastern China.[J]. Journal of Beijing Forestry University, 2013, 35(4): 21-26.
[6]	WANG Xiu-wei, MAO Zi-jun. Effects of conducting tissue structure on sap flow density and stem CO2 efflux.[J]. Journal of Beijing Forestry University, 2013, 35(4): 9-15.
[7]	WANG Lian-chun, , ZHAI Ming-pu, LIU Dao-ping, ZHOU Zhi-feng. Relationship between stock sap flow rate of Zizyphus acidojujuba Hu and environmental factors.[J]. Journal of Beijing Forestry University, 2009, 31(6): 134-138.
[8]	CHEN Chong, LI Ji-yue, , WANG Yu- tao. Variation of stem sap flow of Salix matsudana and its impact factors.[J]. Journal of Beijing Forestry University, 2008, 30(4): 82-88.
[9]	DONG Bai_li, WANG Miao, JIANG Ping, JI Lan-zhu. Relations between water beetle diversity and environmental factors in northern slope of Changbai Mountain, northeastern China.[J]. Journal of Beijing Forestry University, 2008, 30(1): 74-78.
[10]	TIAN Jing-hui, HE Kang-ning, WANG Bai-tian, ZHANG Wei-qiang, YIN Jing. Relationship between transpiration of Platycladus orientalis and environmental factors in semi-arid region on Loess Plateau[J]. Journal of Beijing Forestry University, 2005, 27(3): 53-56.

Cited By

Cited by

Periodical cited type(27)

1.	杨佳悦，丁国玉，田秀君. 最大熵模型在物种生境预测中的应用研究进展. 应用生态学报. 2025(02): 614-624 .
2.	杨福成，洪兆春，丁红秀，邵明勤. 基于最大熵模型的鸳鸯潜在越冬分布区预测. 湿地科学. 2024(01): 60-71 .
3.	陈飞，张杰京，王利繁，熊朝永，樊辉. 中国和老挝跨境区域亚洲象生境时空变化及保护成效. 生态学报. 2024(22): 10222-10233 .
4.	刘夫仁，赛罕，贺伟，张正一，冯中华，张梓琪，鲍伟东. 内蒙古赛罕乌拉自然保护区马鹿夏季肠道菌群多样性. 野生动物学报. 2023(01): 90-97 .
5.	张洁，王凯，张翼，严如玉，杨军飞，刘星辰，计勇. 潦河中源段水生条背萤生境适宜性评价. 安徽农业科学. 2023(03): 52-56+61 .
6.	宋慧芳，党晓宏，高永，蒙仲举，孙艳丽. 基于MaxEnt模型的内蒙古自治区樟子松潜在分布研究. 四川农业大学学报. 2023(02): 203-208+248 .
7.	张杰京，陈飞，谢菲，张鑫，尹文萍，樊辉. 亚洲象生境长时序变化及其对人象冲突的影响——基于融合MaxEnt与HSI模型的生境适宜性评价方法. 生态学报. 2023(09): 3807-3818 .
8.	张旭，王玲玲，黄峰，田玉清，杨涛，张玉铭，刘亚恒. 长江中游地区麋鹿生境适宜性分析与生态廊道构建. 华中师范大学学报(自然科学版). 2023(03): 404-411 .
9.	刘伟，李旭琴，李忠伦，李英，油志远，蒋勇，阮光华，鲁碧耕，杨楠. 四川贡嘎山中华斑羚和中华鬣羚的时空分布及重叠性. 应用生态学报. 2023(06): 1630-1638 .
10.	申立泉，吴佳忆，周鑫，吕青昕，王燕群，袁乃秀，孟秀祥. 基于MaxEnt模型的北京周边山区野生狍(Capreolus pygargus)的生境适宜性评价. 生态学杂志. 2023(10): 2555-2560 .
11.	吕环鑫，夏少霞，顾婧婧，苏常红，王春晓，崔鹏. 基于MaxEnt模型的仙居县大型兽类和珍稀鸟类栖息地适宜性评价. 生态学杂志. 2023(11): 2797-2805 .
12.	邹珮雯，徐昉. 生态安全格局构建及景观生态风险预测——以赛罕乌拉国家级自然保护区为例. 生态学报. 2023(23): 9981-9993 .
13.	杨超，范韦莹，蔡晓斌，王学雷，张玉铭，李鹏飞. 基于MaxEnt模型的湖北石首麋鹿国家级自然保护区散养麋鹿夏季生境适宜性评价. 长江流域资源与环境. 2022(02): 336-344 .
14.	秦委，张虹，杨明霞，罗汉，江素萍，刘守金. 基于MaxEnt模型和ArcGIS的东南茜草潜在分布研究. 中国中医药信息杂志. 2022(05): 1-4 .
15.	刘红彩，赵纳勋，庄钰琪，杨梅玲，赵惠茹，叶新平. 基于MaxEnt模型的秦岭山地斑羚生境适宜性评价. 生态学报. 2022(10): 4181-4188 .
16.	滕扬，张沼，张书理，杨永昕，贺伟，王娜，张正一，鲍伟东. 大兴安岭南段马鹿生境适宜性分析与生态廊道构建. 生态学报. 2022(14): 5990-6000 .
17.	王金凤，徐基良，李建强，周春发，邓文洪. 基于动物适宜栖息地的北京市自然保护地保护成效评估. 生态学报. 2022(19): 7807-7817 .
18.	刘艳华，李卫东，张子栋，梁卓，杨娇，牛莹莹，周绍春. 黑龙江省老爷岭南部野猪种群现状及栖息地适宜性. 生态学杂志. 2022(11): 2208-2215 .
19.	刘夫仁，贺伟，赛罕，冯中华，杨永昕，张正一，鲍伟东. 同域分布马鹿与中华斑羚的冬夏季食物构成比较. 动物学杂志. 2022(06): 845-854 .
20.	孙珊，齐增湘，周敏，白瑾瑾，吕婧玮，刘鑫. 野生哺乳动物生态廊道构建——以云豹为例. 安徽农业科学. 2021(07): 81-84+96 .
21.	陈智强，赵增辉，王远飞，韦力，丁国骅，林植华. 基于红外相机技术和MaxEnt模型的黑麂(Muntiacus crinifrons)活动节律分析和潜在适生区预测. 生态学报. 2021(09): 3535-3547 .
22.	钟雪，杨楠，张龙，程跃红，冯茜，胡强，金义国. 卧龙国家级自然保护区绿尾虹雉种群分布和生境质量评价. 四川动物. 2021(05): 509-516 .
23.	郭飞龙，徐刚标，卢孟柱，孟艺宏，袁承志，郭恺琦. 基于MaxEnt模型分析胡杨潜在适宜分布区. 林业科学. 2020(05): 184-192 .
24.	龚旭，付强，王磊，杨彪，张全建，张远彬. 四川鞍子河保护地水鹿和羚牛栖息地适宜性评价与重叠性分析. 生态学报. 2020(14): 4842-4851 .
25.	李响，张成福，贺帅，王雨晴，苗林. MaxEnt模型综合应用研究进展分析. 绿色科技. 2020(14): 14-17 .
26.	张晓晨，邵长亮，葛炎，陈晨，徐文轩，杨维康. 新疆卡拉麦里山有蹄类野生动物自然保护区夏季蒙古野驴适宜生境与种群数量评估. 应用生态学报. 2020(09): 2993-3004 .
27.	Jing Yang，Guo-Fen Zhu，Jian Jiang，Chang-Lin Xiang，Fu-Li Gao，Wei-Dong Bao. Non-invasive genetic analysis indicates low population connectivity in vulnerable Chinese gorals:concerns for segregated population management. Zoological Research. 2019(05): 439-448 .

Other cited types(24)

Get Citation

PDF

XML

Article views (1113) PDF downloads (91) Cited by(51)

Turn off MathJax

Article Contents

Abstract

References

Panthera unica recognition based on data expansion and ResNeSt with few samples