摘要
【目的】改进PSPnet语义分割模型在无人机农田场景下的性能。【方法】对PSPnet语义分割模型进行3方面改进:(1)通过不同维度特征级联,在强化场景解析的基础上保留更多图像细节特征。(2)利用深度可分离卷积模块构建轻量级语义分割模型,使其更加高效。(3)改进激活函数,提升模型分割效果。【结果】所建模型的平均像素准确率和平均交并比分别为89.48%和82.38%,比改进前的模型提高了18.12%和18.93%,且分割结果优于Unet和DeeplabV3+等模型。【结论】改进后的模型能够有效进行无人机遥感农田场景语义分割。
【Background and objective】Deep Learning is an important method in artificial intelligence but has limited applications in agriculture. This paper aims to fill this technology gap based on farmland images acquired using unmanned aerial vehicle(UAV) and the PSPnet segmentation method. 【Method】Different dimensional features in the UAV images were concatenated based on the principle of preserving as many detailed image features as possible via the enhanced scene analysis. A lightweight semantic segmentation model was built using the deep separable convolution module. Finally, the activation function was replaced to improve the segmentation effect of the model. 【Result】Experimental results show that the mean pixel accuracy and the mean intersection over the union of our proposed method are 89.48% and 82.36%, respectively, and their associated segmentation accuracy was improved by 18.12% and 18.93%, respectively. Overall, the segmentation of the proposed method was better than that of Unet and Deeplab V3+.【Conclusion】The proposed method can effectively segment the farmland images acquired by UAV.
作者
刘尚旺
张杨杨
蔡同波
唐秀芳
王长庚
LIU Shangwang;ZHANG Yangyang;CAI Tongbo;TANG Xiufang;WANG Changgeng(College of computer and information engineering,Henan Normal University,Xinxiang 453007,China;Farmland Irrigation Research Institute,Chinese Academy of Agricultural Sciences,Xinxiang 453002,China;Henan Engineering Laboratory of‘Smart Business and Internet of Things Technology’,Xinxiang 453007,China)
出处
《灌溉排水学报》
CSCD
北大核心
2022年第4期101-108,共8页
Journal of Irrigation and Drainage
基金
河南省高等学校重点科研项目(21A520022)。
关键词
PSPnet
语义分割
特征级联
深度可分离卷积
激活函数
PSPnet
semantic segmentation
feature concatenate
deep separable convolution
activation function
