Detection of pine wilt disease infected pine trees using YOLOv5 optimized by attention mechanisms and loss functions

作者：Xiaotong Dong, Li Zhang, Chang Xu, Qing Miao, Junsheng Yao, Fangchao Liu, Huiwen Liu, Ying‐Bo Lu, Ran Kang, Bin Song · 发表于：Ecological Indicators · 年份：2024 · DOI：10.1016/j.ecolind.2024.112764 · 被引用次数：16 · 研究领域：Smart Agriculture and AI、Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications

• Developed YOLO-based network by six attention mechanisms to enhance PWD detection. • SE, CBAM and NAM significantly make model focus on critical features. • Using SIoU and WIoU loss functions to capture color character and improve detection. • Adding random backgrounds enhances generalization capability and precision of model. Pine Wilt Disease (PWD) is one of the most dangerous and destructive disease in the global forest ecosystems. Based on a dataset of pine wilt disease infected trees that we collected and produced, we developed new technology derived from YOLOv5s to promote the detection performance of the PWD infected trees in this work, in which attention mechanisms, random backgrounds and modifications of the loss functions are integrated. In our strategy, six different attention mechanisms, i.e., SE, CA, CBAM, ECA, SimAM and NAM, are added to improve the detection of YOLOv5s algorithm. These mechanisms are added by embedding in the previous layer of the spatial pyramid pooling-fast structure and replacing all C3 layers in the backbone, respectively. All attention mechanisms added in various ways improves the detection results of PWD infected pine trees. Among them, SE, CBAM and NAM attention mechanisms show the most significant improvements. Because all these three attention mechanisms can specifically enhance the ability of the model to focus on the critical feature for densely distributed or complex pine forests with red broad-leaved trees with diseased and withe...