无人机影像的松材线虫病半监督学习检测方法

王畅, 熊汉江, 涂建光, 郑先伟

王畅, 熊汉江, 涂建光, 郑先伟. 无人机影像的松材线虫病半监督学习检测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220634
引用本文: 王畅, 熊汉江, 涂建光, 郑先伟. 无人机影像的松材线虫病半监督学习检测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220634
WANG Chang, XIONG Hanjiang, TU Jianguang, ZHENG Xianwei. Semi-Supervised Learning for Pine Wilt Disease Detection in UAV Images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220634
Citation: WANG Chang, XIONG Hanjiang, TU Jianguang, ZHENG Xianwei. Semi-Supervised Learning for Pine Wilt Disease Detection in UAV Images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220634

无人机影像的松材线虫病半监督学习检测方法

Semi-Supervised Learning for Pine Wilt Disease Detection in UAV Images

  • 摘要: 松材线虫病是对森林具有破坏性威胁的全球性疾病,每年在中国造成了极大的生态和经济损失,快速准确地监测和绘制松树的感染状况对于控制此类病虫害传播至关重要。为解决在少量有病害标签数据下用大量无标签数据提高模型精确率的问题,本文提出了一种基于深度学习的半监督目标检测方法,联合已有的标记数据和非标记数据训练YOLOv5目标检测模型,利用大范围无人机快拼图像快速自动地识别定位出感染松材线虫病的单株变色木。实验结果表明,半监督深度学习和无人机遥感结合能有效识别出疫木,精确率可达到85%以上,漏检率为9%,经过实际业务数据盲盒验证,算法指标满足大面积松材线虫病疫情快速动态监测要求。
    Abstract: Objectives: Pine Wilt Disease (PWD) is a global disease with devastating threat to forests, causing great ecological and economic losses in China every year. It is important to monitor and map the infection status of pine trees accurately for industry sectors to control the spread of such diseases timely. Traditional PWD detection methods such as manual inspection and visual interpretation of remote sensing images are inefficient. Recently, deep learning algorithms have applied in PWD detection task, bringing significant performance gains. However, the current methods usually adopt supervised learning strategy, which requires lots of professional labeling that is time-consuming and labor-intensive. To this end, we proposed a semi-supervised PWD detection method in a teacher-student mechanism. Methods: First, we collected 16310 images in Wudu River Region in Yichang City, Hubei Province, labeled 697 images for model training, and utilized unlabeled 15613 images for testing; then, the YOLOv5 model was adopted as a student model to learn features of input images, the learnt features were updated by exponential moving average algorithm to build a teacher model for generating pseudo-labels of unlabeled test images. The test images with pseudo-labels were mixed with train dataset and fed into the student model to optimize features and generated a stronger teacher model. With the corporation of student and teacher model, the network was more capable to learn robust features for detecting PWD in UAV images. Results: Extensive experiments on the dataset demonstrated that our semi-supervised method could effectively detect the diseased wood, achieving a promising performance with precision of 85.05% and recall of 91.36% on the test dataset, the ground truth of which was provided by third-party professionals. Conclusions: This paper proposed a semi-supervised object detection method using a small portion of labeled data to robustly learn features in large scale UAV images for high-quality PWD detection. The performance on test dataset showed that our method could be well applied to the application of rapid dynamic monitoring of PWD epidemics in forestry field, which could benefit for the sustainable development of forest ecosystem.
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出版历程
  • 收稿日期:  2023-06-03
  • 网络出版日期:  2023-07-02

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