激光与光电子学进展, 2020, 57 (2): 021508, 网络出版: 2020-01-03   

基于Faster R-CNN深度网络的油菜田间杂草识别方法 下载: 1372次

Recognition Method for Weeds in Rapeseed Field Based on Faster R-CNN Deep Network
作者单位
安徽农业大学信息与计算机学院智慧农业技术与装备安徽省重点实验室, 安徽 合肥 230036
摘要
为自动识别油菜田间的杂草,提出基于Faster R-CNN深度网络的油菜田间杂草识别方法,利用COCO数据集的深度网络模型进行迁移训练。首先,以自然环境条件下的油菜与杂草图像为样本,利用Faster R-CNN深度网络模型共享卷积特征,对比VGG-16、ResNet-50和ResNet-101这3种特征提取网络的结果;该方法还与采用3种相同特征提取网络的SSD深度网络模型进行对比。结果表明,基于VGG-16的Faster R-CNN深度网络模型在油菜与杂草目标识别中具有明显的优势,其油菜与杂草的目标识别精确度可达83.90%,召回率达到78.86%,F1值为81.30%。该深度学习方法能够有效实现油菜与杂草目标的准确、高效识别,为多类型杂草目标识别的研究提供了参考。
Abstract
The purpose of this study is to develop a method for automatically identifying weeds in a rapeseed field. We propose a weed-recognition method based on a Faster R-CNN (region-convolution neural network) deep network and use the deep network model of the COCO dataset for migration training. First, by obtaining images of rapeseed and weed samples under natural environment, the Faster R-CNN deep network model is utilized to share the convolution characteristics and the results of three feature extraction networks: VGG-16, ResNet-50, and ResNet-101, are compared. At the same time, the method is also compared with a single shot multibox detector (SSD) deep network model, which includes the three identical feature extraction networks. The results show that the Faster R-CNN deep network model based on VGG-16 has obvious advantages in rapeseed and weed target recognition. The accuracy of target recognition and recall rate of the rapeseed and weeds are 83.90% and 78.86%, respectively, whereas the F1 value is 81.30%. The proposed deep learning method can effectively and rapidly identity rapeseed and weed targets, providing a reference for further research into multi-type weed target recognition.

张乐, 金秀, 傅雷扬, 李绍稳. 基于Faster R-CNN深度网络的油菜田间杂草识别方法[J]. 激光与光电子学进展, 2020, 57(2): 021508. Zhang Le, Jin Xiu, Fu Leiyang, Li Shaowen. Recognition Method for Weeds in Rapeseed Field Based on Faster R-CNN Deep Network[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021508.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!