Water Level Estimation Based on Image of Staff Gauge in Smart City

Xu, Zhikang; Feng, Jing; Zhang, Zhizheng; Duan, Chaofan

doi:10.1109/smartworld.2018.00233

Cited by 11 publications

(13 citation statements)

References 4 publications

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“…The accuracy at night was lower but varied less. Various methods show accuracies of about 1 cm [19][20][21][22][23][24]. However, these methods use a staff gauge to create a strong contrast between the water surface and the stream wall, as well as to serve as a reference for measurements.…”

Section: Resultsmentioning

confidence: 99%

“…The authors used the difference between two adjacent regions of interest in the gray image, first with coarse regions to detect a zone for the waterline and then with fine positioning of the waterline. Xu et al [22] proposed to improve the waterline detection accuracy by identifying the characters on the staff gauge image through a neural network. Image recognition with a staff gauge is also used in [23,24], obtaining a measurement error of 0.9 cm.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Water Level in Urban Streams under Bad Weather Conditions

Azevedo

Brás

2021

Sensors

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Flood control and water resources management require monitoring the water level in rivers and streams. Water level measurement techniques increasingly consider image processing procedures. Most of the systems use a staff gauge to support the waterline detection. However, these techniques can fail when applied to urban stream channels due to water undulation, debris on the water surface, and traces of rain captured by the camera, and other adverse effects on images can be quite dramatic on the results. The importance of considering these effects is that they are usually associated with the variation in the water level with the occurrence of rain. The technique proposed in this work uses a larger detection zone to minimize the effects that tend to obstruct the waterline. The developed system uses an infrared camera to operate during the day and night. Images acquired in different weather conditions helped to evaluate the proposed technique. The water level measurement accuracy was about 1.8 cm for images taken during the day and 2.8 cm for images taken at night. During short periods of heavy rain, the accuracy was 2.6 cm for the daytime and 3.4 cm for the nighttime. Infrared lighting can improve detection accuracy at night. The developed technique provides good accuracy under different weather conditions by combining information from various detection positions to deal with waterline detection issues.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of Water Level in Urban Streams under Bad Weather Conditions

Azevedo

Brás

2021

Sensors

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“…Being flood monitoring a continuous task, we believe that solutions should take night-operation into consideration as we did. Generally, this system is going to be installed into unconstrained and not-standardised contexts, hence, it can't rely on strong chromatic assumptions, like the solutions proposed by [16], which requires the gauge to be brighter than its background, by [17], whose processing steps rely on color and morphology of the gauge, by [21] that exploits color for extracting gauge's ROI. Our solution is not based on chromatic assumptions.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, water level estimation relies on the assumption that the pole is brighter than anything in the Region of Interest (ROI) framed, making this solution not versatile at all. Similarly, in [17] it is presented an automatic solution for detecting gauge's ROI, but it exploits the color and morphology of a specific kind of gauge, which is not the one commonly installed on generic sites. Zhang et al [18] described a system whose performances have been tested during complex conditions.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision System for Staff Gauge in River Flood Monitoring

et al. 2021

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Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

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“…Ensuring daily monitoring, remote dispatching and disaster alert are important measures to make good use of and protect water resources and water conservancy facilities, which provide favorable conditions for water-level measurement based on video images [12,13,14]. There are also some automatic water-level monitoring systems based on image [15,16,17,18]. Image-based methods use image processing instead of human eyes to detect water line readings automatically, and are divided into the following two kinds:…”

Section: Introductionmentioning

confidence: 99%

Visual Measurement of Water Level under Complex Illumination Conditions

Zhang

Zhou

Liu

et al. 2019

Sensors

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Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

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Water Level Estimation Based on Image of Staff Gauge in Smart City

Cited by 11 publications

References 4 publications

Measurement of Water Level in Urban Streams under Bad Weather Conditions

Measurement of Water Level in Urban Streams under Bad Weather Conditions

A Computer Vision System for Staff Gauge in River Flood Monitoring

Visual Measurement of Water Level under Complex Illumination Conditions

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