Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8
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...
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MDPI AG
2025-03-01
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| author | Peidong Jiang Lai Jiang Fengyan Wu Tengteng Che Ming Wang Chuandong Zheng |
| author_facet | Peidong Jiang Lai Jiang Fengyan Wu Tengteng Che Ming Wang Chuandong Zheng |
| author_sort | Peidong Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa72aa03e08344779813dee2cbc2f532 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-aa72aa03e08344779813dee2cbc2f5322025-08-20T02:09:17ZengMDPI AGSensors1424-82202025-03-01257219910.3390/s25072199Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8Peidong Jiang0Lai Jiang1Fengyan Wu2Tengteng Che3Ming Wang4Chuandong Zheng5Hubei Water Resources Research Institute, Wuhan 430070, ChinaHubei Water Resources Research Institute, Wuhan 430070, ChinaHubei Water Resources Research Institute, Wuhan 430070, ChinaHubei Water Resources Research Institute, Wuhan 430070, ChinaHubei Water Resources Research Institute, Wuhan 430070, ChinaYangxin County Bureau of Water Resources and Lakes, Huangshi 435200, ChinaWith 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.https://www.mdpi.com/1424-8220/25/7/2199soil-dwelling termite damageimage recognitionlight weightimproved YOLOv8 model |
| spellingShingle | Peidong Jiang Lai Jiang Fengyan Wu Tengteng Che Ming Wang Chuandong Zheng Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 Sensors soil-dwelling termite damage image recognition light weight improved YOLOv8 model |
| title | Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 |
| title_full | Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 |
| title_fullStr | Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 |
| title_full_unstemmed | Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 |
| title_short | Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 |
| title_sort | target detection method for soil dwelling termite damage based on mcd yolov8 |
| topic | soil-dwelling termite damage image recognition light weight improved YOLOv8 model |
| url | https://www.mdpi.com/1424-8220/25/7/2199 |
| work_keys_str_mv | AT peidongjiang targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 AT laijiang targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 AT fengyanwu targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 AT tengtengche targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 AT mingwang targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 AT chuandongzheng targetdetectionmethodforsoildwellingtermitedamagebasedonmcdyolov8 |