Termites pose a threat to water conservancy infrastructure due to their concealed, recurring, and long-term destructive behaviors. Consequently, implementing termite monitoring in critical hydrological facilities—such as reservoirs—is essential for timely prevention and control. Owing to its real-time efficiency, environmental sustainability, and overall effectiveness, the intelligent termite monitoring system has gradually emerged as a pivotal strategy for termite management. This paper systematically expounds upon the fundamental principles, overarching architecture, and key features of the current intelligent termite monitoring technology in China. Drawing on its practical application in the Suokoutan Reservoir, we detail the specific implementation procedures and examine the corresponding outcomes. Furthermore, an in-depth analysis of the system’s prospective development is presented, providing feasible references and scientific guidance for advancing termite monitoring, early warning, and control in water conservancy projects.

With global climate change and the deterioration of the ecological environment, the safety of hydraulic engineering faces severe challenges, among which soil-dwelling termite damage has become an issue that cannot be ignored. Reservoirs and embankments in China, primarily composed of earth and rocks, are often affected by soil-dwelling termites, such as Odontotermes formosanus and Macrotermes barneyi. Identifying soil-dwelling termite damage is crucial for implementing monitoring, early warning, and control strategies. This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. A lightweight module, known as dimension-aware selective integration (DASI), was added in the neck part to reduce computation time and memory consumption, enhancing detection accuracy and speed. The model was verified using a dataset of 2096 images from the termite damage survey in hydraulic engineering within Hubei Province in 2024, along with images captured by drone. The results showed that the improved YOLOv8 model outperformed four traditional or enhanced models in terms of precision and mean average precision for detecting soil-dwelling termite damage, while also exhibiting fewer parameters, reduced redundancy in detection boxes, and improved accuracy in detecting small targets. Specifically, the MCD-YOLOv8 model achieved increases in precision and mean average precision of 6.4% and 2.4%, respectively, compared to the YOLOv8 model, while simultaneously reducing the number of parameters by 105,320. The developed model is suitable for the intelligent identification of termite damage in complex environments, thereby enhancing the intelligent monitoring of termite activity and providing strong technical support for the development of termite control technologies.