Abstract
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.
Highlights
The use of remote sensing images to inspect illegal cultivation of opium poppy plants is an important technique in the combat against drugs, while satellite remote sensing plays a critical role in detecting opium poppy cultivation
The use of unmanned aerial vehicles (UAV) to inspect opium poppy cultivation has become the main method to fight against illegal opium poppy cultivation
In view of the current situation, this paper proposed a new target detection network Global Multiscale-YOLOv3 on the basis of convolutional neural network to inspect the opium poppy cultivation, which achieved an accuracy of 92.42% in the test dataset
Summary
A large amount of inspection image data collected need to be manually screened and analyzed This process consumes a lot of manpower and material resources, but is 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. 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. Any unauthorized cultivation of opium poppy in mainland China is an illegal act. The use of remote sensing images to inspect illegal cultivation of opium poppy plants is an important technique in the combat against drugs, while satellite remote sensing plays a critical role in detecting opium poppy cultivation
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