Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images
文献类型:期刊论文
作者 | Ma, Jiarong2; Hu, Zhuowei2; Shao, Quanqin1,3; Wang, Yongcai2; Zhou, Yanqiong2; Liu, Jiayan4; Liu, Shuchao1,3 |
刊名 | DIVERSITY-BASEL |
出版日期 | 2022-08-01 |
卷号 | 14期号:8页码:22 |
关键词 | HRNet MMdetection large herbivores UAV remote sensing image overlapping segmentation |
DOI | 10.3390/d14080624 |
通讯作者 | Hu, Zhuowei(huzhuowei@cnu.edu.cn) |
英文摘要 | Algorithm design and implementation for the detection of large herbivores from low-altitude (200 m-350 m) UAV remote sensing images faces two key problems: (1) the size of a single image from the UAV is too large, and the mainstream algorithm cannot adapt to it, and (2) the number of animals in the image is very small and densely distributed, which makes the model prone to missed detection. This paper proposes the following solutions: For the problem of animal size, we optimized the Faster-RCNN algorithm in terms of three aspects: selecting a HRNet feature extraction network that is more suitable for small target detection, using K-means clustering to obtain the anchor frame size that matches the experimental object, and using NMS to eliminate detection frames that have sizes inconsistent with the size range of the detection target after the algorithm generates the target detection frames. For image size, bisection segmentation was used when training the model, and when using the model to detect the whole image, we propose the use of a new overlapping segmentation detection method. The experimental results obtained for detecting yaks, Tibetan sheep (Tibetana folia), and the Tibetan wild ass in remote sensing images of low-altitude UAV from Maduo County, the source region of the Yellow River, show that the mean average precision (mAP) and average recall (AR) of the optimized Faster-RCNN algorithm are 97.2% and 98.2%, respectively, which are 9.5% and 12.1% higher than the values obtained by the original Faster-RCNN. In addition, the results obtained from applying the new overlap segmentation method to the whole UAV image detection process also show that the new overlap segmentation method can effectively solve the problems of the detection frames not fitting the target, missing detection, and creating false alarms due to bisection segmentation. |
资助项目 | National Natural Science Foundation of China[42071289] ; National Key Research and Development Program of China[2018YFC1508902] |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000847036200001 |
资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/182180] |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Hu, Zhuowei |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Terr Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Beijing Jiaotong Univ, Sch Environm, Beijing 100044, Peoples R China |
推荐引用方式 GB/T 7714 | Ma, Jiarong,Hu, Zhuowei,Shao, Quanqin,et al. Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images[J]. DIVERSITY-BASEL,2022,14(8):22. |
APA | Ma, Jiarong.,Hu, Zhuowei.,Shao, Quanqin.,Wang, Yongcai.,Zhou, Yanqiong.,...&Liu, Shuchao.(2022).Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images.DIVERSITY-BASEL,14(8),22. |
MLA | Ma, Jiarong,et al."Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images".DIVERSITY-BASEL 14.8(2022):22. |
入库方式: OAI收割
来源:地理科学与资源研究所
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