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CMRD-Net: a deep learning-based Cnaphalocrocis medinalis damage symptom rotated detection framework for in-field survey

文献类型： 外文期刊

作者： Chen, Tianjiao 1 ; Wang, Rujing 1 ; Du, Jianming 1 ; Chen, Hongbo 1 ; Zhang, Jie 1 ; Dong, Wei 4 ; Zhang, Meng 5 ;

作者机构： 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China

2.Univ Sci & Technol China, Sci Isl Branch, Hefei, Peoples R China

3.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei, Peoples R China

4.Anhui Acad Agr Sci, Agr Econ & Informat Res Inst, Hefei, Peoples R China

5.Jingxian Plantat Technol Extens Ctr, Jingxian Plant Protect Stn, Xuancheng, Peoples R China

关键词： Cnaphalocrocis medinalis; damage symptom; deep learning; rotated object detection; horizontal object detection

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCI

摘要： The damage symptoms of Cnaphalocrocis medinalis (C.medinalis) is an important evaluation index for pest prevention and control. However, due to various shapes, arbitrary-oriented directions and heavy overlaps of C.medinalis damage symptoms under complex field conditions, generic object detection methods based on horizontal bounding box cannot achieve satisfactory results. To address this problem, we develop a Cnaphalocrocis medinalis damage symptom rotated detection framework called CMRD-Net. It mainly consists of a Horizontal-to-Rotated region proposal network (H2R-RPN) and a Rotated-to-Rotated region convolutional neural network (R2R-RCNN). First, the H2R-RPN is utilized to extract rotated region proposals, combined with adaptive positive sample selection that solves the hard definition of positive samples caused by oriented instances. Second, the R2R-RCNN performs feature alignment based on rotated proposals, and exploits oriented-aligned features to detect the damage symptoms. The experimental results on our constructed dataset show that our proposed method outperforms those state-of-the-art rotated object detection algorithms achieving 73.7% average precision (AP). Additionally, the results demonstrate that our method is more suitable than horizontal detection methods for in-field survey of C.medinalis.

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