基于语义分割的非结构化田间道路场景识别  

Recognition of unstructured field road scene based on semantic segmentation model

作　　者：孟庆宽[1] 杨晓霞 张漫[2] 关海鸥 Meng Qingkuan;Yang Xiaoxia;Zhang Man;Gan Haiou(College of Automation and Electrical Eengineering,Tianjin University of Technology and Education,Tianjin 300222,China;Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 10083,China;College of Electrical and Information,Heilongjiang Bayi Agricultural University,Daqing 163319,China)

机构地区：[1]天津职业技术师范大学自动化与电气工程学院,天津300222 [2]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083 [3]黑龙江八一农垦大学电气与信息学院,大庆163319

出　　处：《农业工程学报》2021年第22期152-160,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31571570、62001329);天津市自然科学基金项目(18JCQNJC04500、19JCQNJC01700);天津职业技术师范大学校级预研项目(KJ2009、KYQD1706)。

摘　　要：环境信息感知是智能农业装备系统自主导航作业的关键技术之一。农业田间道路复杂多变,快速准确地识别可通行区域,辨析障碍物类别,可为农业装备系统高效安全地进行路径规划和决策控制提供依据。该研究以非结构化农业田间道路场景为研究对象,根据环境对象动、静态属性进行类别划分,提出一种基于通道注意力结合多尺度特征融合的轻量化语义分割模型。首先采用Mobilenet V2轻量卷积神经网络提取图像特征,将混合扩张卷积融入特征提取网络最后2个阶段,在保证特征图分辨率的基础上增加感受野并保持信息的连续性与完整性;然后引入通道注意力模块对特征提取网络各阶段特征通道依据重要程度重新标定;最后通过空间金字塔池化模块将多尺度池化特征进行融合,获取更加有效的全局场景上下文信息,增强对复杂道路场景识别的准确性。语义分割试验表明,不同道路环境下本文模型可以对场景对象进行有效识别解析,像素准确率和平均像素准确率分别为94.85%、90.38%,具有准确率高、鲁棒性强的特点。基于相同测试集将该文模型与FCN-8S、SegNet、DeeplabV3+、BiseNet模型进行对比试验,该文模型的平均区域重合度为85.51%,检测速度达到8.19帧/s,参数数量为2.41×10^(6),相比于其他模型具有准确性高、推理速度快、参数量小等优点,能够较好地实现精度与速度的均衡。研究成果可为智能农业装备在非结构化道路环境下安全可靠运行提供技术参考。Environmental information perception has been one of the most important technologies in agricultural automatic navigation tasks,such as plant fertilization,crop disease detection,automatic harvesting,and cultivation.Among them,the complex environment of a field road is characterized by the fuzzy road edge,uneven road surface,and irregular shape.It is necessary to accurately and rapidly identify the passable areas and obstacles when the agricultural machinery makes path planning and decision control.In this study,a lightweight semantic segmentation model was proposed to recognize the unstructured roads in fields using a channel attention mechanism combined with the multi-scale features fusion.Some environmental objects were also classified into 12 categories,including building,person,vehicles,sky,waters,plants,road,soil,pole,sign,coverings,and background,according to the static and dynamic properties.Furthermore,a mobile architecture named MobileNetV2 was adopted to obtain the image feature information,in order to reduce the model parameters for a higher reasoning speed.Specifically,an inverted residual structure with lightweight depth-wise convolutions was utilized to filter the features in the intermediate expansion layer.In addition,the last two stages of the backbone network were combined with the Hybrid Dilated Convolution(HDC),aiming to increase the receptive fields and maintain the resolution of the feature map.The hybrid dilated convolution with the dilation rate of 1,2,and 3 was used to effectively expand the receptive fields,thereby alleviating the“gridding problem”caused by the standard dilated convolution.A Channel Attention Block(CAB)was also introduced to change the weight of each stage feature,in order to enhance the class consistency.The channel attention block was used to strengthen both the higher and lower level features of each stage for a better prediction.In addition,some errors of semantic segmentation were partially or completely attributed to the contextual relationship.A pyramid pooli

关 键 词：机器视觉 语义分割 环境感知 非结构化道路 轻量卷积 注意力机制 特征融合 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]