@inproceedings{24de5da55ecb4580979a2cff8b8f57f9,
title = "An Autonomous Recognition Platform for Dam Seepage and Collapse Risks Based on Object Detection Model",
abstract = "Dams are one of the critical engineering structures in water resource utilization and flood disaster prevention. During long-term operation, dams may develop dangerous conditions such as water seepage and collapse, posing potential threats to the safety of the project. To promptly detect these hazardous conditions of dam seepage and collapse, we introduce a novel benchmark called DamS3C, for dam anomaly detection. Utilizing the YOLOv11 object detection model, we develop an autorecognition platform for dam seepage and collapse risks. This platform employs a pipeline push-pull streaming method to enable real-time display of detection results across devices within a local area network. Comprehensive and extensive experimental results demonstrate that our approach achieves high recognition accuracy, efficiency, and overall superiority.",
keywords = "Anomaly Recognition, Computer Vision, Machine Learning, Object Detection",
author = "Wei Liang and Jianbing Wang and Qindan Deng and Liang Guo and Yutong Jiang and Tian Wang",
note = "Publisher Copyright: {\textcopyright} 2025 Technical Committee on Control Theory, Chinese Association of Automation.; 44th Chinese Control Conference, CCC 2025 ; Conference date: 28-07-2025 Through 30-07-2025",
year = "2025",
doi = "10.23919/CCC64809.2025.11179490",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7762--7767",
editor = "Jian Sun and Hongpeng Yin",
booktitle = "Proceedings of the 44th Chinese Control Conference, CCC 2025",
address = "美国",
}