摘要
为实现田间复杂环境下农业机器人自主导航作业过程中障碍物快速检测,提出一种基于改进YOLOv5的田间复杂环境下障碍物检测方法。建立包含农业机械、人、羊三类目标障碍物共计6766张图片的农田障碍物数据集;通过k-means聚类算法生成最佳先验锚框尺寸;引入CBAM卷积块注意力模块,抑制目标障碍物周围复杂环境的干扰,增强目标显著度;增加一个检测头,跨层级连接主干特征,增强多尺度特征表达能力,缓解标注对象尺度方差带来的负面影响;使用Ghost卷积替换Neck层中普通卷积,减少模型参数,降低模型复杂度。改进后的模型比YOLOv5s基准模型检测精度提高2.3%,召回率提高3.1%,精确率提高1.9%,参数量降低7%左右,为无人农业机械自主作业过程中导航避障的研究提供技术参考。
In order to realize the rapid detection of obstacles in the process of autonomous navigation of agricultural robots in complex field environments,an obstacle detection method based on improved YOLOv5 in complex field environments is proposed.The farmland obstacle data set containing a total of 6766 images of agricultural machinery,human and sheep objects are established.The optimal prior anchor box size is generated by the k-means clustering algorithm.The CBAM convolution block attention module is introduced to suppress the interference of the complex environment around the target obstacle and enhance the target saliency.A detection head is added to connect the backbone features across levels,enhance the ability to express multi-scale features,and alleviate the negative impact of the variance of the scale of the labeled objects.The Ghost convolution is used to replace the ordinary convolution in the Neck layer to reduce the model parameters and decrease the model complexity.Compared with the YOLOv5s benchmark model,the improved model has increased the detection accuracy by 2.3%,the recall rate by 3.1%,the accuracy rate by 1.9%,and has decreased the reference number by about 7%.It provides technical reference for the research of navigation and obstacle avoidance during autonomous operation of unmanned agricultural machinery.
作者
杨昊霖
王其欢
李华彪
耿端阳
武继达
姚艳春
Yang Haolin;Wang Qihuan;Li Huabiao;Geng Duanyang;Wu Jida;Yao Yanchuan(College of Agricultural Engineering and Food Science,Shandong University of Technology,Zibo,255000,China)
出处
《中国农机化学报》
北大核心
2024年第6期216-222,256,F0002,共9页
Journal of Chinese Agricultural Mechanization
基金
国家重点研发计划(2021YFD2000502)
山东省现代农业产业技术体系岗位专家项目(SDAIT—02—12)。
