A wheat spike detection method based on Transformer
文献类型：期刊论文
| 作者 | Zhou, Qiong3,4,5; Huang, Ziliang3,5; Zheng, Shijian2,3; Jiao, Lin1,3; Wang, Liusan3; Wang, Rujing3,5
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| 刊名 | FRONTIERS IN PLANT SCIENCE
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| 出版日期 | 2022-10-20 |
| 卷号 | 13 |
| ISSN号 | 1664-462X |
| 关键词 | deep learning IoU loss function transformer wheat spike detection agriculture |
| DOI | 10.3389/fpls.2022.1023924 |
| 通讯作者 | Jiao, Lin(ljiao@ahu.edu.cn) ; Wang, Liusan(lswang@iim.ac.cn) ; Wang, Rujing(rjwang@iim.ac.cn) |
| 英文摘要 | Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs). However, traditional CNNs equip with the inductive bias of locality and scale-invariance, which makes it hard to extract global and long-range dependency. In this paper, we propose a Transformer-based network named Multi-Window Swin Transformer (MW-Swin Transformer). Technically, MW-Swin Transformer introduces the ability of feature pyramid network to extract multi-scale features and inherits the characteristic of Swin Transformer that performs self-attention mechanism by window strategy. Moreover, bounding box regression is a crucial step in detection. We propose a Wheat Intersection over Union loss by incorporating the Euclidean distance, area overlapping, and aspect ratio, thereby leading to better detection accuracy. We merge the proposed network and regression loss into a popular detection architecture, fully convolutional one-stage object detection, and name the unified model WheatFormer. Finally, we construct a wheat spike detection dataset (WSD-2022) to evaluate the performance of the proposed methods. The experimental results show that the proposed network outperforms those state-of-the-art algorithms with 0.459 mAP (mean average precision) and 0.918 AP(50). It has been proved that our Transformer-based method is effective to handle wheat spike detection under complex field conditions. |
| WOS关键词 | DENSITY |
| 资助项目 | National Key R&D Program of China ; Natural Science Foundation of Anhui Province ; [2019YFE0125700] ; [2208085MC57] |
| WOS研究方向 | Plant Sciences |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:000879007100001 |
| 资助机构 | National Key R&D Program of China ; Natural Science Foundation of Anhui Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/130122]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Jiao, Lin; Wang, Liusan; Wang, Rujing |
| 作者单位 | 1.Anhui Univ, Sch Internet, Hefei, Peoples R China 2.Univ Sci & Technol, Dept Informat Engn Southwest, Mianyang, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China 4.Anhui Agr Univ, Coll Informat & Comp, Hefei, Peoples R China 5.Univ Sci & Technol China, Sci Isl Branch, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Qiong,Huang, Ziliang,Zheng, Shijian,et al. A wheat spike detection method based on Transformer[J]. FRONTIERS IN PLANT SCIENCE,2022,13. |
| APA | Zhou, Qiong,Huang, Ziliang,Zheng, Shijian,Jiao, Lin,Wang, Liusan,&Wang, Rujing.(2022).A wheat spike detection method based on Transformer.FRONTIERS IN PLANT SCIENCE,13. |
| MLA | Zhou, Qiong,et al."A wheat spike detection method based on Transformer".FRONTIERS IN PLANT SCIENCE 13(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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