基于Mask RCNN的城市内涝积水深度智能识别研究OA

Urban waterlogging has become a frequent challenge in many cities,posing significant threats to public safety and urban infrastructure.Traditional methods for monitoring water levels,which often rely on physical sensors or manual inspection,are increasingly recognized as inefficient due to their high operational costs,limited spatial coverage,and inability to provide real-time responses.To overcome these shortcomings,this study introduced an intelligent and automated approach for waterlogging depth estimation using a deep learning-based computer vision framework.The proposed model utilized the Mask RCNN architecture to detect and segment both flooded areas and reference objects in images.A key innovation lies in the use of bicycle wheels as a reference scale object,enabling quantitative water depth estimation through geometric analysis.This approach enhances adaptability in complex urban environments and significantly reduces dependency on expensive specialized equipment.The model was trained on a custom dataset consisting of images of water accumulation scenes under various environmental conditions,along with corresponding reference objects.After training,the system could accurately identify the boundaries of inundated regions and precisely locate bicycle wheels even in complex and occluded scenes.An ellipse-fitting algorithm was then applied to the masked regions of detected wheels to derive geometric parameters such as semi-major and semi-minor axes.These parameters were used to calculate the submergence ratio of the wheel,which correlated directly with the water depth based on pre-established hydraulic relationships.Furthermore,the integration of multi-scale feature extraction within the network improved detection performance across different distances and perspectives.Extensive experiments demonstrate that the model achieves high performance in both detection and segmentation tasks.It attains a precision exceeding 90%for localizing waterlogged areas and bicycle wheels,while the intersection over union(IoU)between predicted masks and ground-truth masks surpasses 70%.The results also indicate that detection accuracy is higher for bicycle wheels viewed from a frontal or lateral perspective compared to angled views,and performance is better for nearer objects compared to those farther from the camera.This research contributes to the field by offering a scalable,cost-effective,and intelligent solution for urban waterlogging monitoring.The proposed model supports large-area coverage and continuous monitoring capabilities,facilitating rapid emergency response and better urban water management.The approach is not only applicable in real-time urban disaster mitigation but also adaptable for integration with smart city platforms,thereby enhancing urban resilience to climate-induced hazards.Future work will focus on optimizing computational efficiency for embedded deployment and incorporating temporal dynamics for flood progression forecasting.