Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3
نویسندگان
چکیده
Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized of opium poppy forbidden. Low-altitude inspection illegal through unmanned aerial vehicle featured with the advantages time-saving high efficiency. However, large amount image data collected need to be manually screened analyzed. This process not only consumes lot manpower material resources, but also subjected omissions errors. In response such problem, this paper proposed an method by adding larger-scale detection box on basis original YOLOv3 algorithm improve accuracy small target detection. Specifically, ResNeXt group convolution was utilized reduce number model parameters, ASPP module added before small-scale model’s ability extract local features obtain contextual information. The test results self-created dataset showed that: mAP (mean average precision) indicator Global Multiscale-YOLOv3 0.44% higher than that (MobileNet) algorithm; total parameters 13.75% 35.04% lightweight network (MobileNet). Overall, had reduced increased recognition accuracy. It provides technical support for rapid accurate processing in low-altitude remote sensing inspection.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13112130