The invention belongs to the technical field of tobacco
plant number statistics, and discloses a tobacco
plant segmentation and counting method and
system based on unmanned aerial vehicle large-scale images. According to the method, on the basis of an unmanned aerial vehicle large-scale tobacco field segmentation image
data set, a
feature extraction complex MobileNetV2 and a channel attention mechanism ECA are integrated on a DeeplabV3 + network, and a tobacco field segmentation framework MED-Net is constructed to realize tobacco field segmentation; a tobacco
plant background removing method is further utilized to remove
background information; in addition, the invention also establishes a tobacco plant contour
screening method based on a pixel level, which is beneficial to screening of
small target contours such as weeds and the like, and improves the extraction accuracy of the tobacco plant contour. According to the method, tobacco field segmentation and tobacco plant counting in the large-scale unmanned aerial vehicle
remote sensing image can be effectively achieved, the over-segmentation problem caused by the fact that the unmanned aerial vehicle
remote sensing image needs to be
cut in a large amount due to insufficient equipment performance is solved, and statistics of the tobacco plant number is achieved on the premise that a large amount of
data labeling is not needed; and the tobacco plant statistics efficiency and accuracy are improved.