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基于语义分割和融合算法的草坪杂草定位

Lawn weed localization based on semantic segmentation and fusion algorithms

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[目的]探究基于多算法融合的草坪杂草精准定位算法,为草坪杂草的自动识别和清除机器人的除草作业提供技术支撑与理论依据.[方法]提出了一种基于语义分割的多算法融合的草坪定位算法.首先,通过PSPNet网络分割草坪和非草坪轮廓.其次,针对分割出来的非草坪轮廓提取感兴趣的区域,去除非杂草轮廓,保留杂草轮廓.然后,利用Zhang-Suen细化算法提取杂草轮廓骨架线,并获取骨架交叉点数量和坐标位置.最后,利用融合算法依据交叉点数量选择不同的定位策略,实现杂草根部的精准定位.[结果]融合算法定位的杂草坐标与真实杂草根部中心坐标的均方根误差为83.17像素,相比平均质心法减少了14％,相比最小外接圆减少了22％.换算到实际场景之下,融合算法定位的杂草坐标与真实杂草根部中心坐标的均方根误差为12.48 mm,误差在可接受的误差范围内.融合算法提高了杂草根部中心的定位精度,降低了杂草定位的误差.[结论]基于语义分割和融合算法的草坪杂草定位方法提高了杂草根部中心的定位精度,降低了单一方法的定位误差,可以为草坪杂草自动识别和除草作业提供技术支持.
[Objective]The precise location algorithm of lawn weeds based on a fusion algorithm was explored to provide theoretical basis and technical support for lawn weed automatic identification and removal robots to carry out weeding operations.[Method]A multi-method fusion algorithm based on semantic segmentation for lawn location was proposed.First,the PSPNet network segmented the lawn and non-lawn contours.Secondly,for the segmented non-lawn contours,the interest area was extracted,excluding the non-weed contours and retaining the weed contours.Then,Zhang-Suen thinning algorithm extracted weed contour skeleton lines and obtained the number and coordinates of skeleton intersections.Finally,The fusion algorithm selected different positioning strategies according to the number of intersecting points to achieve accurate positioning of weed roots.[Result]The root-mean-square error between the location coordinates of weeds by fusion algorithm and the actual root center coordinates of weeds was 83.17 Px,and reduced by 14％ compared with the method of mean centroid,reduced by 22％ compared with the method of a minimum circumscribed circle.Converted to the actual scenario,the root-mean-square error between the location coordinates of weeds by fusion algorithm and the actual root center coordinates of weeds was 12.48 mm,which was within the acceptable error range.The fusion algorithm can improve the accuracy of weed root center location,and reduce the error of weed location.[Conclusion]The location method of lawn weeds based on semantic segmentation and fusion algorithm can improve the localization accuracy of weed root centers,reduce the positioning error by a single method,and provide technical support for automatic identification and removal of lawn weeds.

image analysissemantic segmentationalgorithmslawn weed localizationfusion algorithmZhang-Suen refinement extractionmethod of mean centroida minimum circumscribed circle

郭朋、李文彬、徐道春、白效鹏、王梓耘

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林业装备与自动化国家林业和草原局重点实验室,北京林业大学工学院,北京 100083

图像处理 语义分割 算法 草坪杂草定位 融合算法 Zhang-Suen细化提取 平均质心法 最小外接圆

国家重点研发计划

2018YFD0700601

2024

10.12171/j.1000-1522.20240057

北京林业大学学报
北京林业大学

北京林业大学学报

CSTPCD北大核心
影响因子：1.237
ISSN：1000-1522
年,卷(期)：2024.46(7)
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