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Interactive image segmentation based field boundary perception method and software for autonomous agricultural machinery path planning

文献类型： 外文期刊

作者： Wang, Hao 1 ; Ma, Zhifeng 4 ; Ren, Yaxin 1 ; Du, Siqi 1 ; Lu, Hao 1 ; Shang, Yehua 1 ; Hu, Shupeng 1 ; Zhang, Guangqiang 1 ; Meng, Zhijun 1 ; Wen, Changkai 1 ; Fu, Weiqiang 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.State Key Lab Intelligent Agr Power Equipment, Beijing 100097, Peoples R China

4.Beijing Inst Technol, Beijing 100081, Peoples R China

关键词： Orthoimagery; Instance and semantic segmentation; UAV; Weakly supervised deep learning; Semi-supervised learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 217 卷

页码：

收录情况： SCI

摘要： Autonomous agricultural machinery path planning requires high-precision field boundary information. To address the challenge of rapidly acquiring accurate information about different types of land objects in complex field scenarios, this study introduces an interactive segmentation-based method and software for agricultural field boundary perception, specifically designed for high-resolution orthophotos. The method aims to accurately delineate various agricultural objects in the image, such as crops, soil, roads, edges, exits to fields, and obstacles. Compared to previous research on agricultural object detection and segmentation, this study proposes an interactive deep image segmentation model for perceiving multiple types of agricultural features. During the image segmentation process, manually adding positive and negative points provides supervised information for the segmentation of agricultural images. In addition, this research uses the PaddlePaddle deep learning framework to implement the proposed method and extends the open-source software EISeg to develop a dedicated tool for agricultural image segmentation. Through 3 to 4 interactive iterations, the method achieves an impressive mean Intersection over Union (mIoU) segmentation accuracy of about 90%. The model's average inference time on the training server was 0.197 s, meeting the real-time requirements of the interactive segmentation method. By accurately segmenting agricultural land features from high-resolution orthoimagery, the proposed method can provide valuable support for the construction of high-precision navigation maps for autonomous agricultural machinery.

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