无线电工程

为了发展精细农业并降低农业种植的人工成本,利用无人机遥感影像实现了农作物产量的智能化测量系统。该系统利用低空无人机拍摄农作物的遥感图像,将图像传入卷积神经网络进行特征提取。将卷积神经网络特征图传入特征金字塔网络提取不同尺度的特征图,再将每个尺度的特征图送入不同形状的滑动窗口处理,将特征图拼接起来。实验结果表明,该测量系统能够准确地测量农作物的数量与位置,测量精度较Faster R-CNN提升了约16.2%。

In order to develop the precision agriculture and reduce the labor cost of agricultural planting, the remote sensing images of unmanned aerial vehicle are used to implement an intelligent measurement system of crop yield.The system utilizes low-altitude unmanned aerial vehicle to capture remote sensing images of crops.Then the captured images are fed into the convolutional neural networks to extract features.Secondly, the feature maps of the convolutional neural networks are delivered to the feature pyramid networks to extract feature maps of different scales, and the feature maps of each scale are transferred to sliding windows with different shapes for processing, finally the feature maps are flatten and concatenated.Experimental results show that the designed system can measure and locate the crops accurately.The measurement precision is improved about 16.2% compared to Faster R-CNN.