4.7 Article

E2CropDet: An efficient end-to-end solution to crop row detection

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 227, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.120345

关键词

Machine vision; Autonomous navigation; Crop row detection; Deep learning; Image processing

向作者/读者索取更多资源

Crop row detection is crucial for visual navigation of agricultural machinery. In this study, a compact and efficient deep learning-based network named E2CropDet is proposed, which models each crop row as an independent object, enabling an end-to-end detection process with no post-processing. The network utilizes generic feature extractors and line-shaped proposals for detection, achieving remarkable results and a detection speed of 166 frames per second.
Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typi-cally developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature ex-tractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据