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Semantic segmentation and path planning for orchards based on UAV images

文献类型： 外文期刊

作者： Sun, Qilin 1 ; Zhang, Ruirui 2 ; Chen, Liping 2 ; Zhang, Linhuan 2 ; Zhang, Hongming 1 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing 100097, Peoples R China

5.Beijing Acad Agr & Forestry Sci, Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Semantic segmentation; Orchard path planning; Deep learning; SADNet

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 200 卷

页码：

收录情况： SCI

摘要： A new semantic segmentation network, Space Atrous Depthwise Network (SADNet), was proposed to segment orchard unmanned aerial vehicle (UAV) images. In SADNet, a depthwise identity convolution block (DIC) was introduced to optimise the network feature extraction capabilities. An atrous spatial pyramid pooling module (ASPP) was introduced to fuse multi-scale semantic information. To improve the receptive field of the network, the concurrent spatial and channel squeeze and excitation module (scSE) were also introduced to focus the network on the pixels that need attention. The performance of SADNet on the orchard dataset was found to be better than that of DeepLabv3, PSPNet, and R2U-Net. The pixel accuracy and mean intersection over union reached 93.61 % and 88.28 %, respectively. MIoU of SADNet reached 86.2 % on the public dataset Pascal VOC 2012. Then, based on the results of orchard segmentation, we proposed a new method for constructing orchard raster maps. Based on this method, we planned the intra- and inter-row paths of the orchard. The planned paths have the smallest root mean square error of 3.19 m from the optimal path. The orchard segmentation and path planning method based on UAV images can plan the path of the whole orchard at the same time and can assist agricultural robots in pesticide spraying and fruit harvesting.

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