基于半监督学习的松林变色疫木检测方法

针对模型训练中数据标注成本过高的问题,提出一种基于无人机图像分析的半监督变色疫木目标检测方法。该方法提出级联抗噪声半监督目标检测模型（Cascade Noise-Resistant Semi-supervised object detection,CNRS）,使用抗噪声学习提升模型对伪标签的学习质量;通过级联网络解决训练中正负样本的分布问题;使用ResNet50和特征金字塔网络结构增强模型对多尺寸和小目标疫木的识别能力;在监督学习阶段使用FocalLoss,提升网络对边缘目标和早期疫木等困难样本的学习,使用SmoothL1Loss保证梯度相对稳定;在RCNN阶段使用软化非极大抑制软化检测框剔除过程。该文提出的半监督目标检测模型CNRS使用训练集中半数标注的数据进行训练,试验结果表明,最优模型在测试集上的平均精度（Average Precision,AP）可达87.7%,与Faster RCNN使用完全标注数据相比,标注量减少了50%,且AP提升了2.3个百分点,与同时期最先进的半监督模型Combating Noise相比,AP提升了1.6个百分点。该方法在准确检出多种不同形态疫木的基础上,大幅度降低了数据标注成本,为农林病虫害防治提供了可靠的数据支持。

Method for detecting pine forest discoloured epidemic wood based on semi-supervised learning

Abstract: Deep learning has been a promising technology for epidemic tree detection in recent years. However, the expensive data annotation has posed a great challenge to the discolored epidemic wood detection in the pine forest. Particularly, some difficulties are still found in the dataset expansion, model generalization ability, and the presence of samples with obscured or small objects during detection. In this study, target detection was proposed for the discolored epidemic wood using semi-supervised learning and Unmanned Aerial Vehicle (UAV) image analysis. The specific procedure mainly included the dataset for the pine forest epidemic wood, semi-supervised model training, and algorithm detection. The dataset was collected from three provinces in China, especially with a total of six epidemic wood forms. Two datasets were randomly and equally divided for the training and testing in the supervised and semi-supervised learning stages, respectively. 2 160 training and 240 testing sets were available after data augmentation. The anti-noise loss (SoftFocalLoss and L1Loss) was classified with the uncertainty indicator for the data distillation. Among them, combating Noise was the most advanced semi-supervised target detection model in the same period. As such, the quality of pseudo labeling was improved effectively. The following improvements were achieved in the Cascade Noise-Resistant Semi-supervised (CNRS) object detection model, compared with the Combating Noise. 1) A cascade network was added to balance the distribution of positive and negative samples during training, in order to equalize the accuracy and over-fitting. 2) The FocalLoss was used to mine the difficult samples in the phase of supervised learning. The improved learning of the model was achieved in the difficult objects, such as the edge objects and early epidemic wood. 3) SmoothL1Loss was used to ensure a relatively stable gradient, particularly for the large difference between the true and the predicted value. 4) Soft-Non-Maximum Suppression (Soft-NMS) was used to soften the rejection process of the detection frame in the RCNN stage, in order to prevent the near targets from being filtered. The experiments were conducted on Ubuntu 18.04 operating system using NVIDIA Tesla P100 graphics processor. The experimental results show that the Average Precision (AP) values were 64.2%, and 85.4%, respectively, for the single-stage detector SSD300 and the two-stage detector Faster RCNN using fully labeled data. 50% of the labeled data was also selected in the semi-supervised model. Both AP values were higher than the fully supervised model, indicating that the anti-noise learning effectively extracted the semantic information from the pseudo labels. The ablation model of CNRS Model1 improved the AP by 0.3 percentage points, where a cascade network was added with the RoI Pooling. Model2 further improved the AP by 0.3 percentage points after the RoI Align. The AP of the optimal model on the test set was 87.7% with an F-Score of 0.669. A comparison was also made on the detection of the four models using 24 test images. The error detection rate of CNRS was compared with the fully supervised network model, and Faster RCNN using fully labeled data. The CNRS presented a 50% reduction in the labeling, and a 2.3 percentage point increase in the AP, which was 1.6 percentage points higher than that of the semi-supervised network Combating Noise. This improved model can also provide reliable data support for pest control in agriculture and forestry. An accurate detection can be achieved in many different forms of epidemic trees and a significant reduction in the data labeling cost.