AQUAVISION: COMPUTER VISION-BASED DRAIN WATER LEAKAGE DETECTION SYSTEM

Abstract

ABSTRACT “AQUAVISION: COMPUTER VISION-BASED DRAIN WATER LEAKAGE DETECTION SYSTEM” The present invention is directed to a computer vision-based system for drain water leakage detection employing real-time monitoring wherein, the system comprises: a live video capturing and analysis system. It shall include: resolution cameras installed at every possible leakage point; a computer vision processing unit with OpenCV; a detection and notification system consisting of further subcomponents: a machine learning model YOLO (You Only Look Once); a live alerting system; a user interface that is data visualizing. The present invention also encompasses a method to detect and monitor water leaks using the computer vision and machine learning technology. The present disclosure hereinafter relates to a highly advanced monitoring system that uses such complex computer vision algorithms and ML models in order to detect water leakages in real-time along with instantaneous notification functionalities that could alert in response to provide timely actions preventing further wastage of the precious resource. The system to be disclosed in the present invention helps municipalities, residential complexes, and industrial facilities monitor and manage their water infrastructure efficiently and reduce water wastage and related costs through early detection and prevention of leakage. Figure 1

Specification

Description:FIELD OF INVENTION

The present invention relates to the field of a computer vision-based system for drain and road water leakage detection. More generally, this is a real-time monitoring system based on OpenCV-YOLO (You Only Look Once) algorithms, run for the automatic detection and notification of domestic carelessness-induced water leakages.
BACKGROUND OF INVENTION
Water leakage from drains and roads caused due to negligence by people in domestic premises is one of the major problems in modern infrastructure developed in developing countries like India. The traditional methods currently used for such leakages are based on manual inspection or traditional monitoring systems, which are often inefficient and reactive rather than proactive. According to various studies, a significant loss of water occurs when leakages remain hidden or are detected after their happening in public infrastructure. In many areas, the identification of water leakages depends on visual inspection by maintenance personnel, requiring them to physically visit sites, which leads to delayed responses and increased water wastage. Conventional sensor-based systems, while available, often lack real-time detection features and can be costly to implement across large areas.
The existing solutions such as wireless sensor networks, ultrasonic methods, and acoustic detection systems are correlated with low accuracy, slow response time, and poor scalability. These systems do not necessarily provide instant visual verification of leakages and perform unwell in urban complex environments. The practical application of computer vision and artificial intelligence in the modern water leakage detection systems is very limited, even though such system has great potential to improve the accuracy and response time concerning the detection. Consequently, there is a need for developing a system that would be able to overcome the drawbacks existing in the prior arts through the application of advanced computer vision and machine learning technologies for the real-time detection of water leakage.
PRIOR ART
US10943357B2 discloses a video-based system for detection of indoor liquid leaks on equipment, like pumps, using color investigation in video feeds. A color video camera captures images of a field of view specified by the camera; wherein said camera generates video frames having pixel values that represent at least some colors of objects in the video frames. The video feed is taken by the image analyzer with a comparison between pixel values and a selected previous target color related to the suspected leaking fluid, including color similarity and brightness. After a match is found, the image analyzer sends a notification alert to allow operators to be able to review captured footage remotely to confirm the leak and take necessary action to arrest it.
The WO2020181662A1 discloses a privacy-protective monitoring system and method for the detection of abnormal behaviors or events. The system involves a module for sensing, feature extraction, and abnormal behavior/event detection. The system captures monitoring data from a scene, which goes to the feature extraction module where the gathered monitoring information is processed to generate a real-time feature stream. Such a feature stream is analyzed by a pertained deep neural network model within the detection module. The inventors protect privacy by replacing video with feature data, which reduces the size of data and the transmission speed while supporting cloud security and allowing flexible updates that meet different requirements.
The US20210216852A1 patent describes an AI-driven method and system for detecting leaks in pipelines carrying liquids or gases. The system involves a computer that receives two distinct datasets: one from normal pipeline operations and another obtained by simulating leaks through controlled releases of the liquid or gas at multiple pipeline locations. The system is trained using such datasets, with any leak and no-leak conditions annotated for each. This method allows the system to recognize leak signatures along with the detection of leaks correctly. They increase the reliability of real-time pipeline monitoring and maintenance through the use of this method.
US11430322B2 is a building water-leak detection and alert system that has sensors and an analytical engine intended to check for water leaks. It includes a memory and a communication interface that can be connected to the water-flow sensors mounted on building pipes or to humidity, temperature, or liquid-water sensors for determining if there is the presence of water in specific areas. The analysis engine uses the data from sensors to conclude leaks and their location in the building; it then sends instructions to a valve that controls water flow within the house. Wherever a leak is detected, it can send a control signal that results in the shutdown of the valve nearest to the site of the leak, thus preventing further water damage.
OBJECTS OF THE INVENTION
The primary aim of the current invention is to have a computer vision based system to detect leakages in drain water using high processing algorithms and machine models.
Another object of the invention is towards formulating real-time monitoring that is integrated with OpenCV and YOLO algorithms into automatic detection and reporting of leakages.
Another aim of the invention is to provide an apparatus that provides a user-friendly interface, along with a visual representation of leakage rate real time and historical analyses on leakage patterns in water.
Another object of the invention is to create a system where water leakage can be prevented proactively through the detection of risk factors prior to becoming critical issues.
Another object of the invention is to provide a scalable and flexible system which, at least in principle, could be rolled out between homes to vast industrial complexes.
Yet another objective of the present invention is to place on the market a system which provides automatic, self-reporting alarms and messages to relevant authorities where possible leaks have been identified, so action could quickly be taken to prevent damage.
These and other objects and advantages of the present subject matter will become apparent to a person skilled in the art after consideration of the following detailed description taken into consideration with accompanying drawings in which preferred embodiments of the present subject matter are illustrated.
SUMMARY OF THE INVENTION
The present invention relates to a computer vision-based system for detecting
drain water leakages, providing real-time monitoring and automated alerts.
According to one aspect of the present invention, a leakage detection system for drain waters (AquaVision) is described which embodies: a real-time network monitoring high-resolution cameras module, advanced computer vision module based on OpenCV image-processing, and YOLO-based machine learning model which can detect objects and recognize leakage; further, a user interface system is also provided for data visualization and alert management. Another aspect of the invention here is a method for detecting leaks in a drainage system, using a computer vision-based system comprising:
Capturing real-time video footage through strategically placed cameras; processing the video feed using OpenCV algorithms for preliminary image analysis; detecting and classifying various water leakage patterns using YOLO-based machine learning models; generating real-time alerts and notifications when leakages are detected; providing real-time visualization of leakage data through a user interface; and maintaining historical records of leakage patterns for preventive analysis. The system facilitates proactive leakage management by incorporating sophisticated image processing and also machine learning techniques, hence real-time monitoring capability and immediate alert mechanisms is provided in relation to the prevention of water wastage as well as infrastructures.
BRIEF DESCRIPTION OF THE DRAWINGS
It is to be noted, however, that the appended drawings illustrate only typical
embodiments of the present subject matter and are therefore not to be considered for limiting of its scope, for the invention may admit to other equally effective embodiments. The detailed description is described with reference to the accompanying figure. In the figure, reference numbers are used consistently to identify like features and sub-components throughout the description. An embodiment of the system or method of the present subject matter is now described, by way of example, and with reference to the accompanying figure, in which:
Figure 1 represents a schematic architecture diagram for the AquaVision system, incorporating camera setup, computer vision processing unit, module for machine learning, alert mechanism, and user interface for detecting water leakage.
DETAILED DESCRIPTION
Various modifications and alternative forms, specific embodiment thereof have been shown by way of example in the figure and will be described below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
Figure 1 represents the schematic architecture diagram of the AquaVision system for drain water leakage detection using computer vision. From the diagram, it is evident that the systems have successfully been integrated with other subsystems including the following:
• Strategic placement of high-resolution cameras for monitoring probable leakage points.
• Computer Vision Processing Unit that incorporates OpenCV for real-time video analysis as well as image processing.
• Machine Learning Module that features YOLO algorithms for advanced object detection and anomaly recognition.
• Real-time Leakage Detection Alert and Notification Mechanism.
• User Interface and Data Visualization Component running live feeds and analytics.
• Data Storage and Analysis Unit to preserve historical data and patterns.
As shown in the architectural diagram, these components are continuously communicating with one another in real-time to:
a. Capture visual data through the camera network.
b. Process the captured footage using computer vision algorithms.
c. Analyze the processed data using machine learning models.
d) Leakage detection is done automatically in real-time, alerts correspondingly issued.
e) Communication to users through a user-friendly interface.
f) Historical water usage data is recorded and used for predictive maintenance.
This inclusive system architecture would thus ensure leak detection efficiently, respond quite fast, and proactively preventive plan through an all-in-one water management and monitoring system.

, Claims:CLAIMS
We claim;
1. A computer vision-based drain water leakage detection system comprising:
a) real-time monitoring system consisting of:
High-resolution cameras placed at the potential leakage points;
Computer vision processing unit using OpenCV;
b) Analysis and notification system comprising:
a YOLO machine learning model;
Mechanism for an immediate alert;
an interface with data visualizing abilities.
2. The computer vision-based system as claimed in claim 1 wherein, YOLO machine learning model and OpenCV were learned to search for normal water flow and leakage patterns,
3. The Computer vision-based system of claim 1 wherein the user interface gives real-time analytics and historical data visualization regarding the water usage.
4. A method for detecting drain water leakage using a computer vision-based system comprising:
a) Capturing Video footages of real-time leakage points through high-resolution cameras;
b) Algorithms of OpenCV for real-time video feed toward preliminary detection and segmentation;
c) Analyzing the processed data employing YOLO machine learning model to check whether there is any leakage pattern or not;
d) Classification of the detected anomalies into various classes like normal water flow or leakage;
e) Automatically sending instant warnings and notifications of the leakage during the detection process;
f) Displaying real-time analytics and historical data through the user interface for the purpose of monitoring and decision-making.
5. The method of claim 4 wherein, on detecting a potential leakage, the system renders instantaneous alerts to the concerned authorities and users.
6. The method as claimed in claim 4 wherein, the system consistently tracks and updates history data for possible predictive analysis within the possible future leakages.
7. The computer vision-based system of claim 1 wherein, the system is flexible and scalable for implementation at residential, commercial, and industrial areas.

Documents