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Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

文献类型： 外文期刊

作者： Xia, Lang 1 ; Zhang, Ruirui 1 ; Chen, Liping 3 ; Li, Longlong 3 ; Yi, Tongchuan 3 ; Wen, Yao 1 ; Ding, Chenchen 1 ; Xie, 1 ;

作者机构： 1.Natl Res Ctr Intelligent Equipment Agr, Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

3.Beijing Res Ctr Intelligent Equipment Agr, Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

4.Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

5.Shandong Ruida Pest Control Co Ltd, Jinan 250000, Peoples R China

关键词： deep learning; image segmentation; pine wilt disease; infected pine DeepLabv3+; focal loss

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 18 期

页码：

收录情况： SCI

摘要： Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease's degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder-decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

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