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Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

文献类型： 外文期刊

作者： Wang, Qian 1 ; Qin, Wuchang 2 ; Liu, Mengnan 3 ; Zhao, Junjie 2 ; Zhu, Qingzhen 1 ; Yin, Yanxin 2 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

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

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

4.Qinghai Univ, Coll Mech Engn, Xining 810003, Peoples R China

关键词： wheat harvesting; semantic segmentation; harvest boundary line recognition; autonomous driving; machine vision

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0 similar to 1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

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