摘要
田间除草技术在农业生产中具有重要意义。针对复杂背景下农作物与杂草识别率低、算法鲁棒性差等问题,提出一种图像分割网络Res-Unet。该网络为unet网络的改进版本,采用resnet50网络代替unet主干网络,解决复杂背景下农作物与杂草区域提取困难、小植株检测效果差、分割边缘震荡、变形问题。将图像的平均交并比、准确率、训练时长作为评价指标进行实验。结果表明:使用Res-Unet模型的平均交并比为82.25%,平均像素准确率为98.67%。改进的Res-Unet模型相对于Unet平均交并比高出4.74%,相较于segnet平均交并比高出10.68%,训练时间减少3小时。该方法对复杂背景下甜菜杂草检测效果良好,可为机器人精确除草提供参考。
The field weeding technology is of great significance in agricultural production.The traditional weed identification technology has the disadvantages of low efficiency or great limitations.To solve the problem of low recognition rate of crops and weeds in complex background and poor robustness of algorithm,an image segmentation network res UNET is proposed.Res UNET is an improved version of UNET network.It uses resnet50 network instead of the main network of UNET to solve the problem of crop and weed area extraction under complex background,poor detection effect of small plants,edge vibration and deformation of segmentation.The average intersection ratio,accuracy and training time of the image are selected as evaluation indexes.The results show that the average cross union ratio of res UNET model is 83.25%,and the average pixel accuracy is 98.67%.The improved res UNET model is 4.74%higher than the UNET average,10.68%higher than the segnet average,and the training time is reduced by 3 hours.This method has a good detection effect on beet weeds in complex background,and can provide a reference for the follow-up robot precision weeding.
作者
尚建伟
蒋红海
喻刚
陈颉颢
王博
李兆旭
张伟平
SHANG Jian-wei;JIANG Hong-hai;YU Gang;CHEN Jie-hao;WANG Bo;LI Zhao-xu;ZHANG Wei-ping(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,China;78098 Military Training Team,Chengdu 610200,China)
出处
《软件导刊》
2020年第7期127-130,共4页
Software Guide
关键词
图像分割
卷积神经网络
深度学习
图像识别
杂草识别
image segmentation
convolutional neural network
deep learning
image identification
weed identification
作者简介
尚建伟(1992-),男,昆明理工大学机电工程学院硕士研究生,研究方向为机器视觉、深度学习;通讯作者:蒋红海(1974-),男,博士,昆明理工大学机电工程学院讲师、硕士生导师,研究方向为机器视觉。
