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Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3

文献类型： 外文期刊

作者： Wang, Chunshan 1 ; Wang, Qian 1 ; Wu, Huarui 1 ; Zhao, Chunjiang 1 ; Teng, Guifa 2 ; Li, Jiuxi 5 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Hebei Agr Univ, Sch Informat Sci & Technol, Baoding 071001, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Hebei Key Lab Agr Big Data, Baoding 071001, Peoples R China

5.Hebei Agr Univ, Sch Mech & Elect Engn, Baoding 071001, Peoples R China

关键词： poppy inspection; unmanned aerial vehicle; small target detection; low-altitude

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 11 期

页码：

收录情况： SCI

摘要： Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model's ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.

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