摘要
为了保证自动驾驶农机的安全行驶,需要对农田间道路进行高精度识别。该研究以北京市大兴区榆垡镇为研究地点,构建了农田间道路图像数据集,使用开源标注工具Labelme软件进行图像标注,以UNet为基本网络结构,针对分割过程中存在的道路边缘和远处道路分割效果较差等现象,提出了3个改进方向:在编码器网络中添加残差连接,增加网络复杂度;使用池化卷积融合结构完成下采样,增加可训练参数以减少信息损失。试验结果表明,使用ACBlock(Asymmetric Convolution Block,ACBlock)和DACBlock(Dilated Asymmetric Convolution Block,DACBlock)替换UNet中的卷积核,增加了卷积核“骨架”结构的权重和卷积核的感受野,提高了远处道路及道路边缘的分割效果,农田间道路分割的交并比值为85.03%,相较于原UNet提高了6.52个百分点,且高于ResUNet、UNet3+等网络。农机行驶速度在20 km/h左右,该研究网络对于1280×720像素大小的图片平均推理时间为163 ms,符合农机自动驾驶时间复杂度要求。该研究提高了自动驾驶农机对农田间道路的感知能力,为安全行驶提供了信息支持。
Automatic driving of agricultural machinery has drawn much more attention in recent years,particularly with the development of precision farming and the improvement of sensor technologies.Four parts of autonomous driving are positioning,perception,decision-making,and control system.In perception,the road recognition aims to extract the drivable area for the safe driving of agricultural machinery.However,there are no obvious lane markings or signs for field roads,while the road borders are in irregular shape,often shaded by trees.All of these features make it difficult for field road identification,unlike structured urban road.In road recognition,semantic segmentation on the collected road images is a binary classification task of background and road for each pixel to extract the drivable area.In this study,the data in spring and summer was collected in the Yufa Town,Daxing District,Beijing of China.A stereo camera was fixed on the agricultural machine to collect image data.The fixed position ensured that the camera was firm and reliable without being obscured during driving.The fixed height was set to 1.2 m.The driving speed of agricultural machinery was about 5 km/h during data collection.The field roads included semi-structured and unstructured roads.The sunny day was selected to collect data.The collecting time was about 4 hours,and a total of 1600 pictures were captured.The training and test set were divided into the ratio of 4:1.The open-source software Labelme was used for image labeling.UNet was selected as the basic network,due to its simplicity and suitability for binary classification.A better performance was achieved when training on a small data set.Three improvements were also proposed for the UNet.1)An identity mapping channel was established between every two convolutions,and the residual was constructed by adding pixels.The residual connection was used to alleviate the gradient disappearance and explosion during training,while easy the training of deep neural networks.2)A fusion convolutional structure and the maximum pooling were established to replace the maximum pooling layer in the UNet.The useful information in the original image was maximized when halving feature map,where the segmentation of small area features was improved significantly.The inference time of the model was much longer because much more convolution operation increased the training parameters.3)An asymmetric convolution structure was used in ACBlock,where the weight of the"skeleton"structure increased to improve the efficiency of feature extraction in the convolution kernel.Inspired by ACBlock,DACBlock was proposed using the dilated convolution,which further expanded the receptive field of the convolution feature map.ACBlock and DACBlock were used to replace the 3×3 convolution kernel in UNet.As such,the segmentation accuracy of road edge shapes was improved significantly.The hierarchical fusion and batch normalization were used in the inference stage to maintain that the number of parameters and inference time were all the same as the original structure.The improved UNet presented an IOU value of 85.03%for the field road segmentation,higher than the original UNet,ResUNet,and UNet3+.The recognition accuracy was relatively lower under cloudy weather in road junctions,due to insufficient light and occlusion.There was always water in the middle of the road after rain,where a certain degree of reflection occurred on the water under the mirror reflection.Therefore,the water increased the error of road segmentation.In the case of good or weak light in the evening and shade,the road segmentation was performed better for the safe driving of agricultural machinery.The segmentation accuracies of remote roads and road edges were also significantly better than those of other networks.Moreover,the average inference time of the model was 163 ms,meeting the time requirements of automatic driving in agricultural machinery.
作者
杨丽丽
陈炎
田伟泽
徐媛媛
欧非凡
吴才聪
Yang Lili;Chen Yan;Tian Weize;Xu Yuanyuan;Ou Feifan;Wu Caicong(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2021年第9期185-191,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2016YFB0501805)。
关键词
图像分割
机器视觉
深度学习
田间道路
自动驾驶
image segmentation
machine vision
deep learning
field roads
automatic driving
作者简介
杨丽丽,副教授,博士,研究方向为计算机网络与智能信息处理的应用。Email:llyang@cau.edu.cn;通信作者:吴才聪,副教授,博士,研究方向为无人驾驶与协同作业、农机作业大数据挖掘和农机导航与位置服务的应用。Email:wucc@cau.edu.cn。
