电子科技2024，Vol.37Issue(5)：18-24,7.DOI:10.16180/j.cnki.issn1007-7820.2024.05.003
基于深度学习的城市内涝区域车辆检测与分析
Vehicle Detection and Analysis in Urban Waterlogging Area Based on Deep Learning
摘要
Abstract
In the urban waterlogging scene,many people and vehicles are trapped in the water,which brings adverse effects to the public life.With the rapid development of computer technology,deep learning is more and more widely used in solving practical problems.This study proposes a method to build a MaskR-CNN(Regions with Con-volutional Neural Networks Features)model using TensorFlow deep learning framework,which has achieved good de-tection results in the detection of waterlogging areas in urban waterlogging scenes,with the mAP(mean Average Pre-cision)value reaching 89%.Based on the YOLOv5(You Only Look Once version 5)model,the dense interframe difference operation is used to track people and vehicles in waterlogged areas,and the tracking accuracy reached a-bout 90%.Moreover,ResNet(Residual Network)attached to YOLOv5 is used to analyze the risk of submersion of vehicles in waterlogging scenarios.The experimental results show that the vehicle risk detection effect of the proposed model is better than other models.关键词
城市内涝/MaskR-CNN模型/TensorFlow/深度学习/目标检测/YOLOv5/ResNet/危险度分析Key words
urban waterlogging/MaskR-CNN model/TensorFlow/deep learning/target detection/YOLOv5/ResNet/risk analysis分类
建筑与水利引用本文复制引用
夏榕成,刘德儿..基于深度学习的城市内涝区域车辆检测与分析[J].电子科技,2024,37(5):18-24,7.基金项目
国家自然科学基金(42271434) （42271434）
江西省自然科学基金项目(20202BAB202025) National Natural Science Foundation of China(42271434) （20202BAB202025）
Jiangxi Natural Science Foundation Project(20202BAB202025) （20202BAB202025）
