Exploring the potential of visual tracking and counting for trees infected with pine wilt disease based on improved YOLOv5 and StrongSORT algorithm

Abstract

Pine wilt disease (PWD) has been consistently recognized as one of the most catastrophic forest diseases in China over the past four decades. Accurate identification and timely removal of infected pine trees are vital for controlling the disease spread. However, previous studies about the identification of PWD-infected trees still relied on traditional machine learning methods, with static imagery being the predominant data form utilized. Due to diverse forest environments, there are significant errors in wide-range identification and the collaborative adaptation capability between multiple algorithms is suboptimal. Real-time dynamic tracking and counting of PWD-infected trees based on deep learning have received little attention. Thus, an improved YOLOv5 was proposed in this study, which in synergy with StrongSORT, enables the tracking and counting of PWD-infected trees in a dynamic visual way. For this purpose, a dataset of 6,450 static images (39,809 PWD-infected tree samples) was constructed for model training and validation, and 130 dynamic video segments (approximately 210,000 frames) and 674 static images were used to evaluate the proposed method. To enhance feature extraction efficiency in deep learning networks, the Second-Order Channel Attention (SOCA) mechanism was introduced, and the Simplified Spatial Pyramid Pooling-Fast (SimSPPF) was employed as a replacement for the original SPPF. Additionally, for the geometric features of PWD-infected trees, a more scientific Weighted Boxes Fusion (WBF) strategy was utilized during the prediction phase to construct detection boxes, which contributes to better detection of dense targets. Regarding detection, the improved YOLOv5 performs optimally, with mAP@0.5 and F1-Score of 92.4 % and 88.3 %, respectively, an increase of 2.5 % and 1 % compared to the original model. The generalization capability has been evaluated on the test set, all metrics exceeded 90 %. In terms of tracking, the combination of the improved YOLOv5 with StrongSORT yields Identification F1 (IDF1), High-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and Multi-Object Tracking Precision (MOTP) of 75.4 %, 55.6 %, 63.5 %, and 72.3 % respectively, showcasing increase of 3.5 %, 2.7 %, 6 %, and 0.3 % compared to the original model. Notably, the Mostly Lost (ML) and Identity Switches (IDSW) are reduced by 43 % and 20 % respectively. Concerning counting, the proposed method was evaluated on 130 dynamic video segments, indicating a high correlation with the Ground truth (R2 = 0.965), affirming its effectiveness. In summary, visual tracking and counting of PWD-infected trees in complex forest areas can be enabled by the method proposed, providing a new approach for the intelligent monitoring and management of PWD.