Water Level Detection from Video with Fir Filtering

Iwahashi, Masahiro; Udomsiri, Sakol

doi:10.1109/icccn.2007.4317920

Cited by 15 publications

(10 citation statements)

References 2 publications

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“…This process enhances the feature information of the foam line, addressing the issue of missed detections in smaller regions, and ultimately improving the accuracy of foam line detection. (2) In the feature extraction stage, the C3NAM module is proposed, which considers contribution factors to enhance the ability of the model to extract foam line features. (3) In the spatial pyramid part, the B-SPPCSPC module is proposed to enhance the perception degree of feature information in two branches.…”

Section: Relatedmentioning

confidence: 99%

“…Another category is water level measurement based on image processing. Iwahashi et al [2] improved detection accuracy by adding the frame images of moving objects and applying frame averaging to reduce interference in water level line detection. However, this algorithm requires affine transformation for slanted water level lines, making it unsuitable for irregular water level lines.…”

Section: Introductionmentioning

confidence: 99%

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A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

Xu,

Wu,

Guo

2024

Electronics

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During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based on YOLOv5x was proposed, which enhances feature information and improves anchor box regression prediction to accurately detect the position of foam lines. Firstly, in the preprocessing stage, employing a rectangular box to simultaneously label the foam line and the edge of the A/O pool within the same region, enhances the feature information of the foam line. Then, the C3NAM module was proposed, which applies weight sparse penalties to attention modules in the feature extraction section, to enhance the capability of extracting foam line features. Subsequently, a B-SPPCSPC module was proposed to enhance the fusion of shallow and deep feature information, addressing the issue of susceptibility to background interference during foam line detection. Next, the Focal_EIOU was introduced to ameliorate the issue of class imbalance in detection, providing more accurate bounding box predictions. Lastly, optimizing the detection layer scale improves the detection performance for smaller targets. The experimental results demonstrate that the accuracy of this algorithm reaches 98.9%, and the recall reaches 88.1%, with a detection frame rate of 26.2 frames per second, which can meet the actual detection requirements of real-world application scenarios.

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Section: Relatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

Xu,

Wu,

Guo

2024

Electronics

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“…Image-based measurements of discharge and water level have recently been given particular attention although the idea is not new [1][2][3][4][5][6][7][8][9][10][11]. The main advantages compared to other techniques include access to verifiable data, as the human eye can read or verify each data point, the non-contact sensing as the camera is safely placed away from the water, and in the case of small videos, access to the velocity field at the surface of the water.…”

Section: Introductionmentioning

confidence: 99%

Field performance of the GaugeCam image-based water level measurement system

Bírgand

Chapman²,

Hazra³

et al. 2022

PLOS Water

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Image-based stage and discharge measuring systems are among the most promising new non-contact technologies available for long-term hydrological monitoring. This article evaluates and reports the long-term performance of the GaugeCam (www.gaugecam.org) image-based stage measuring system in situ. For this we installed and evaluated the system over several months in a tidal marsh to obtain a good stratification of the measured stages. Our evaluation shows that the GaugeCam system was able to measure within about ±5 mm for a 90% confidence interval over a range of about 1 m in a tidal creek in a remote location of North Carolina, USA. Our results show that the GaugeCam system nearly performed to the desired design of ±3 mm accuracy around 70% of the time. The system uses a dedicated target background for calibration and geometrical perspective correction of images, as well as auto-correction to compensate for camera movement. The correction systems performed well overall, although our results show a ‘croissant-shaped’ mean error (-1 to +4 mm,) varying with water stage. We attribute this to the small, yet present, ‘fish-eye’ effect embedded in images, for which our system did not entirely correct in the tested version, and which might affect all image-based water level measurement systems.

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“…To solve these problems, methods have been proposed for measuring the water level by capturing images of rivers [4,[8][9][10][11]. These methods have the merit that the cost of locating the equipment is low because only general surveillance cameras are needed.…”

Section: Introductionmentioning

confidence: 99%

River state classification combining patch-based processing and CNN

et al. 2020

Self Cite

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This paper proposes a method for classifying the river state (a flood risk exists or not) from river surveillance camera images by combining patch-based processing and a convolutional neural network (CNN). Although CNN needs much training data, the number of river surveillance camera images is limited because flood does not frequently occur. Also, river surveillance camera images include objects that are irrelevant to the flood risk. Therefore, the direct use of CNN may not work well for the river state classification. To overcome this limitation, this paper develops patch-based processing for adjusting CNN to the river state classification. By increasing training data via the patch segmentation of an image and selecting patches that are relevant to the river state, the adjustment of general CNNs to the river state classification becomes feasible. The proposed patch-based processing and CNN are developed independently. This yields the practical merits that any CNN can be used according to each user’s purposes, and the maintenance and improvement of each component of the whole system can be easily performed. In the experiment, river state classification is defined as the following problems using two datasets, to verify the effectiveness of the proposed method. First, river images from the public dataset called Places are classified to images with Muddy labels and images with Clear labels. Second, images from the river surveillance camera in Nagaoka City, Japan are classified to images captured when the government announced heavy rain or flood warning and the other images.

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Water Level Detection from Video with Fir Filtering

Cited by 15 publications

References 2 publications

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

Field performance of the GaugeCam image-based water level measurement system

River state classification combining patch-based processing and CNN

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