基于Faster R-CNN的田间西兰花幼苗图像检测方法  

Image Detection Method for Broccoli Seedlings in Field Based on Faster R-CNN

作　　者：孙哲[1] 张春龙[1] 葛鲁镇 张铭 李伟[1] 谭豫之[1] SUN Zhe;ZHANG Chunlong;GE Luzhen;ZHANG Ming;LI Wei;TAN Yuzhi(College of Engineering,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学工学院

出　　处：《农业机械学报》2019年第7期216-221,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2017YFD0701300)

摘　　要：为解决自然环境下作物识别率不高、鲁棒性不强等问题,以西兰花幼苗为研究对象,提出了一种基于Faster R-CNN模型的作物检测方法。根据田间环境特点,采集不同光照强度、不同地面含水率和不同杂草密度下的西兰花幼苗图像,以确保样本多样性,并通过数据增强手段扩大样本量,制作PASCAL VOC格式数据集。针对此数据集训练Faster R-CNN模型,通过设计ResNet101、ResNet50与VGG16网络的对比试验,确定ResNet101网络为最优特征提取网络,其平均精度为90.89%,平均检测时间249 ms。在此基础上优化网络超参数,确定Dropout值为0.6时,模型识别效果最佳,其平均精度达到91.73%。结果表明,本文方法能够对自然环境下的西兰花幼苗进行有效检测,可为农业智能除草作业中的作物识别提供借鉴。Traditional methods of image processing for crop detection under agricultural natural environment are easily affected by small samples and subjective judgment,so they have many disadvantages such as low recognition rate and low robustness.Deep learning can self-study according to data set,and has a strong ability to express feature.Therefore,a new broccoli seedlings detection approach based on Faster R-CNN model was proposed.Data acquisition was the first step to build deep learning model,and the diversity of data can improve the generalization ability of the model.According to the characteristics of field environment,broccoli seedlings images with different light intensities,different ground moisture contents and different weed densities were collected.The sample size was expanded by images rotation and noise enhancement,and data set was transformed as PASCAL VOC format.And then the Faster R-CNN model was trained by using data set.Contrast experiment was designed on ResNet101,ResNet50 and VGG16 networks.The results showed that ResNet101 network with the deepest network layer and smaller parameter space was the best feature extraction network.The average detection accuracy was 90.89%,and the average time-consuming was 249 ms.Based on that,the network super-parameters were optimized and the average accuracy of model detection reached 91.73%,when Dropout value was 0.6.The results showed that this approach can effectively detect broccoli seedlings in agricultural natural environment,and provided a hopeful solution for crop detection in the field of agriculture.

关 键 词：西兰花幼苗 作物识别 深度学习 卷积神经网络 FasterR-CNN 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]