基于深度学习的城市内涝区域车辆检测与分析

doi:10.16180/j.cnki.issn1007-7820.2024.05.003

Abstract

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 Convolutional Neural Networks Features) model using TensorFlow deep learning framework, which has achieved good detection results in the detection of waterlogging areas in urban waterlogging scenes, with the mAP(mean Average Precision) 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 about 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.

Key words: urban waterlogging, MaskR-CNN model, TensorFlow, deep learning, target detection, YOLOv5, ResNet, risk analysis

CLC Number:

TU992.25

XIA Rongcheng, LIU Deer. Vehicle Detection and Analysis in Urban Waterlogging Area Based on Deep Learning[J].Electronic Science and Technology, 2024, 37(5): 18-24.

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类别标签	数量	图13(a)	图13(b)	图13(c)	图13(d)
safe	27	9	2	1	15
unsafe	1	1	0	0	0
danger	0	0	0	0	0
错检	2	1	0	0	1
漏检	0	0	0	0	0

模型名称	漏检数量	错检数量
RetinaNet	10	4
SSD	9	3
CenterNet-Hourglass	5	2
Faster-RCNN	4	4
Efficientdet-d1	3	2
YOLOv5	0	2

Vehicle Detection and Analysis in Urban Waterlogging Area Based on Deep Learning

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