4.6 Article

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

期刊

ENTROPY
卷 23, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/e23091160

关键词

Mask RCNN; instance segmentation; sobel operator; deep learning

资金

  1. National Natural Science Foundation of China [61771262]
  2. Tianjin Science and Technology Major Project and Engineering [18ZXRHNC00140]
  3. Tianjin Key Laboratory of Optoelectronic Sensor and Sensor Network Technology

向作者/读者索取更多资源

An automatic extraction algorithm for crop images based on Mask RCNN is proposed in this paper, which improves the precision, recall, and F1 scores of crop image extraction results by enhancing the network model structure and optimizing design features.
The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

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

4.6
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