4.7 Article

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

Journal

REMOTE SENSING
Volume 13, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/rs13112130

Keywords

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

Funding

  1. Beijing Municipal Science and Technology Project [Z191100004019007]
  2. Hebei Province Key Research and Development Project [20327402D, 19227210D]
  3. National Natural Science Foundation of China [61871041]
  4. Key projects of science and technology research in colleges and universities of Hebei Province [ZD2018221]

Ask authors/readers for more resources

The proposed method in this paper enhanced the accuracy of small target detection in poppy inspection by adding a larger detection box and optimizing model parameters. Test results showed that the new model outperformed traditional models and improved work efficiency in handling large-scale image data.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available