一种基于深度学习的城市道路积水内涝图像识别方法

A Method for Urban Road Waterlogging Image Recognition Based on Deep Learning

关键词:  内涝  图像识别  深度学习  公共监控视频

Key words:  waterlogging  image recognition  deep learning  public surveillance camera

摘要:

为了提高超大城市积水内涝的监测预警能力，针对现有积水内涝检测方法实用性、实时性存在的不足，依托广州市 9 万余个高密度公共监控视频作为数据基础，基于深度学习的目标检测 RTMDet (Real-TimeModels for Object Detection) 模型建立了一种道路积水内涝图像识别方法，采用多线程技术实现大规模视频点图像的高效采集，同时基于实例分割 RTMDet 模型实现积水内涝目标的快速检测和识别。使用 2018 年 8 月 15日—2020 年 5 月 23 日通过视频接口和互联网收集的共 8 463 张图像样本集构建识别模型，用 2020 年 6 月 25 日—2022 年 9 月 10 日采集的 6 106 张图像对模型进行检验评估。结果表明：强降水过程中，模型能准确识别出具有明显积水内涝特征的图像及积水内涝特征所处图像的位置区域，数据清洗后模型总体识别准确率为 86.60%；光线干扰和降水造成摄像头画面模糊是导致模型虚警的两大主要因素。研究成果已经在广州气象影响预报业务检验和广州市应急指挥决策辅助系统应用，为城市积水内涝自动监测和预警提供有效支撑，对水务、交通等气象业务场景有一定的参考价值。

Abstract:

To enhance the monitoring and early warning capabilities for waterlogging in megacities, this study addressed the low practicality and insufficient real-time performance of existing waterlogging detection methods through the use of a high-density network of over 90,000 public surveillance cameras in Guangzhou. A road waterlogging image recognition method was established based on the RTMDet model, a deep-learning-based object detection algorithm. Multithreading technology was employed to efficiently acquire images from a large number of cameras. The RTMDet model with instance segmentation capabilities enables rapid detection and identification of waterlogging areas. A set of 8463 images, collected between August 15, 2018, and May 23, 2020, was used to develop the recognition model. The model was then validated and evaluated using 6106 images collected between June 25, 2020, and September 10,2022. The results indicate that during intense precipitation, the model can accurately identify images of obvious waterlogging and the locations of these waterlogged areas. After data cleaning, the overall recognition accuracy of the model was 86.60%. Light interference and image blurring due to precipitation were the two primary factors causing false alarms. Currently, this algorithm has been implemented in Guangzhou’s meteorological impact forecasting verification and meteorological decision- making support platform, providing effective support for automatic monitoring and early warning of urban waterlogging. It also offers valuable insights for water management, transportation, and other relevant sectors.