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.
基于YOLOv7算法的易涝区积水快速识别方法研究
- Title:
- A Method for Rapid Identification of Waterlogging in Flood-prone Area Based on YOLOv7
- Keywords:
- urban waterlogging; waterlogging identification; object detection; YOLOv7 algorithm; deep learning
- 摘要:
- 为了能够高效便捷地监测识别城市易涝区积水情况，利用深度学习技术，提出了一种基于YOLOv7算法的快速识别积水方法。采用传统数据增强和Mosaic数据增强方法对训练集图像进行扩充，构建了YOLOv7积水检测模型，并与其他主流目标检测模型Faster R-CNN和YOLOv5m进行了对比分析。结果表明，YOLOv7模型取得了最好的效果，其精度、召回率、平均精度、F1分数分别达到了92.9%、83.4%、88.8%和87.9%，且单张图片推理时间仅约为0.025 s。该方法在城市内涝积水识别与预警方面具有良好的应用前景。
- 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.
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