JINXiao-fei,GUOShuai,HUANGQiong,et al.A Method for Rapid Identification of Waterlogging in Flood-prone Area Based on YOLOv7[J].China Water & Wastewater,2024,40(9):123-128.
A Method for Rapid Identification of Waterlogging in Flood-prone Area Based on YOLOv7
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第40卷
Number:
第9期
Page:
123-128
Column:
Date of publication:
2024-05-01
- Keywords:
- urban waterlogging; waterlogging identification; object detection; YOLOv7 algorithm; deep learning
- Abstract:
- This paper proposed a method for rapid identification of waterlogging based on YOLOv7 algorithm by using deep learning technology, so as to monitor and identify waterlogging in flood-prone area efficiently and conveniently. The traditional and Mosaic data augmentation methods were used to expand the training set images, and the YOLOv7 waterlogging detection model was established. Then, the model was compared with other mainstream object detection models (Faster R-CNN and YOLOv5m). The YOLOv7 model achieved the best performance. Its precision, recall, average precision and F1 score reached 92.9%, 83.4%, 88.8% and 87.9% respectively, and the inference time of a single image was only about 0.025 s. This method demonstrates a good application prospect in the identification and early warning of urban waterlogging.
Last Update:
2024-05-01