Research on Target Detection and Counting Algorithms for Swarming Termites in Agricultural and Forestry Disaster Early Warning

The accurate monitoring of termite swarming—a key indicator of dispersal and population growth—is essential for early warning systems that mitigate infestation risks in agricultural and forestry environments. Automated detection and counting systems have become a viable alternative to labor-intensive and time-consuming manual inspection methods. However, detecting and counting such small and fast-moving targets as swarming termites poses a significant challenge. This study proposes the YOLOv11-ST algorithm and a novel counting algorithm to address this challenge. By incorporating the Fourier-domain parameter decomposition and dynamic modulation mechanism of the FDConv module, along with the LRSA attention mechanism that enhances local feature interaction, the feature extraction capability for swarming termites is improved, enabling more accurate detection. The SPPF-DW module was designed to replace the original network’s SPPF module, enhancing the feature capture capability for small targets. In comparative evaluations with other baseline models, YOLOv11-ST demonstrated superior performance, achieving a Recall of 87.32% and a mAP50 of 93.21%. This represents an improvement of 2.1% and 2.02%, respectively, over the original YOLOv11. The proposed counting algorithm achieved an average counting accuracy of 91.2%. These research findings offer both theoretical and technical support for the development of a detection and counting system for swarming termites.