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Related Experiment Video

Updated: May 14, 2025

Extracting DNA from the Gut Microbes of the Termite Zootermopsis Angusticollis and Visualizing Gut Microbes

Extracting DNA from the Gut Microbes of the Termite Zootermopsis Angusticollis and Visualizing Gut Microbes

Published on: May 28, 2007

Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8.

Peidong Jiang^1,2, Lai Jiang^1,2, Fengyan Wu^1,2

¹Hubei Water Resources Research Institute, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

An improved YOLOv8 model, MCD-YOLOv8, accurately identifies soil-dwelling termite damage in hydraulic engineering. This AI approach enhances monitoring and control strategies for vital infrastructure threatened by termites.

Keywords:

image recognition improved YOLOv8 model light weight soil-dwelling termite damage

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Area of Science:

Engineering
Environmental Science
Computer Science

Background:

Global climate change and ecological decline pose significant threats to hydraulic engineering safety.
Soil-dwelling termites, like Odontotermes formosanus and Macrotermes barneyi, cause substantial damage to earth and rock structures such as reservoirs and embankments.
Effective identification of termite damage is essential for proactive monitoring, early warning systems, and control strategies in hydraulic engineering.

Purpose of the Study:

To develop an advanced object detection model for identifying soil-dwelling termite damage in hydraulic engineering.
To enhance the accuracy and efficiency of termite damage detection using artificial intelligence.

Main Methods:

An improved YOLOv8 model, MCD-YOLOv8, was developed incorporating a Monte Carlo attention (MCA) module and a dimension-aware selective integration (DASI) module.
The MCA module enhances recognition of small targets by generating attention maps via random sampling pooling.
The DASI module optimizes computation time and memory usage, improving detection speed and accuracy.

Main Results:

The MCD-YOLOv8 model demonstrated superior performance compared to traditional and enhanced models in detecting termite damage.
The model achieved significant improvements in precision (6.4%) and mean average precision (2.4%) over the standard YOLOv8.
MCD-YOLOv8 reduced model parameters by 105,320, decreased detection box redundancy, and improved small target detection accuracy.

Conclusions:

The MCD-YOLOv8 model offers a robust solution for intelligent identification of termite damage in complex hydraulic engineering environments.
This AI-driven approach enhances intelligent monitoring of termite activity.
The developed model provides crucial technical support for advancing termite control technologies in infrastructure protection.