基于AI多模态识别的地下被动式白蚁监测装置研发与应用OA

Termite damage to water projects is highly concealed and catastrophic.Traditional termite monitoring methods rely on single-mode sensing,which is vulnerable to environmental interference,suffers from a high miss-detection rate of small targets,and frequent false alarms,and they can hardly meet the needs of safety early warning for water conservancy projects.This paper took the fusion of"vision,sound,and temperature"multimodal data as the core technology to develop a passive termite monitoring device suitable for underground hidden environments.A high-precision multi-source sensing hardware system was constructed to synchronously capture termite's morphological characteristics,weak activity sound signals,and metabolic micro-temperature rise signals.A two-level fusion mechanism of feature-level fusion and decision-level fusion was designed,combined with an optimized YOLOv10-M lightweight object detection model,which significantly improved the robustness of small target recognition for termites.Key technologies such as temperature-humidity joint sensing,termite species and caste recognition,and nest location prediction were added to form a complete multimodal intelligent recognition system.Laboratory tests and field applications in 182 water conservancy projects across China show that the comprehensive termite recognition rate of the device is greater than 99.95%;the alarm accuracy is greater than 98.5%;the false alarm rate is lower than 1.5%,and the missed alarm rate is not higher than 0.3%.The device can operate stably in complex water conservancy scenarios such as-20-55 °C wide temperature,water accumulation,salt spray,and high humidity.The research results achieve a technological leap from single-signal monitoring to multimodal cooperative intelligent recognition and can provide technical support for early identification,accurate early warning,and efficient disposal of termite hazards in water conservancy projects.