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.