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A road ponding image detection and early warning method based on a hybrid model

A road water and image detection technology, applied in the field of big data, can solve the problems of easy damage, low detection accuracy, complicated installation of detection equipment, etc., and achieve the effect of improved accuracy, simple maintenance, and improved detection accuracy

Active Publication Date: 2019-04-23
JIANGSU UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to provide a road water image detection and early warning based on a hybrid model, which solves the technical problems of traditional detection instruments such as complicated installation, easy damage and low detection accuracy

Method used

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  • A road ponding image detection and early warning method based on a hybrid model
  • A road ponding image detection and early warning method based on a hybrid model
  • A road ponding image detection and early warning method based on a hybrid model

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Embodiment Construction

[0053] Depend on Figure 1-Figure 5 A hybrid model-based road water image detection and early warning method shown includes the following steps:

[0054] Step 1: Set several cameras at different positions of the monitored road section, and the cameras take the monitoring image of the monitored road section; the user dynamically marks and sets the waterlogging detection area and the waterlogging level mark area in the monitoring image;

[0055] In order to adapt to the detection of road ponding on road sections and different camera positions, the present invention adopts the user interactive labeling method of the detection area and the water depth level mark, that is, the water level mark area, and the user dynamically sets the detection area in the monitoring image Image and water level marking area, the interactive marking process is as follows figure 2 Depth markers and waterlogged detection zones shown in .

[0056] Step 2: Perform image feature extraction on the input ...

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Abstract

The invention discloses a road ponding image detection early warning method based on a hybrid model, belongs to the technical field of big data, realizes the real-time ponding detection of different monitoring cameras on a road based on deep learning image characteristics, generates the ponding severity level data, and solves the defects of complex installation, easy damage and the like of a traditional detection instrument. An interactive area configuration mode is adopted to adapt to monitoring cameras in different scenes. According to the method, the deep neural network characteristics andthe improved ResNet residual network are adopted, the spatio-temporal characteristics of the monitoring video are combined, the ponding confidence is calculated in a mixed mode, so that the detectionaccuracy is improved, and the requirement for the actual application accuracy is met based on the monitoring video condition.

Description

technical field [0001] The invention belongs to the technical field of big data, and in particular relates to a hybrid model-based road water image detection and early warning method. Background technique [0002] Due to the low terrain or poor drainage of the arterial roads, road water accumulation incidents often occur during the rainy season. Severe water accumulation can easily cause vehicles to stall and break down. Therefore, the highway management department needs to grasp the situation of water accumulation in the road section in real time, and according to the severity of the water accumulation in the road section, make corresponding control strategies such as prohibiting traffic, delaying traffic, and passing by vehicle type. [0003] The current water level monitoring on roads is generally realized by using water level sensors. The water level sensor method can realize quantitative water level detection and can accurately measure the depth of road water accumulat...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/41G06V20/52G06F18/24G06F18/214Y02A50/00
Inventor 陈湘军舒振球叶飞跃范洪辉马晓东
Owner JIANGSU UNIV OF TECH
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