Real-Time Pipe Anomaly Detection Using Optical Flow and Deep Learning
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Solution Overview
Problem
Current anomaly detection systems for pipes are time-consuming and prone to errors due to manual video review, and existing automated solutions fail to accurately detect anomalies in real-time video streams, especially for varying pipe materials and surface conditions over time.
Innovation Solution
A method and system that utilize deep learning techniques to detect anomalies in real-time video streams by eliminating unwanted frames, identifying potential anomaly frames based on optical flow vectors and contours, and classifying them using trained models specific to surface material types, with the ability to adapt through selective re-training based on cumulative feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual video review is used to detect anomalies in pipe surfaces, then operator can identify and tag observed defects, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that uses image processing and machine learning algorithms to detect anomalies in pipe surfaces, eliminating human operators from the direct inspection process while maintaining or improving detection accuracy
Solution Approach 2:
The system enables self-service anomaly detection by automatically analyzing video frames, identifying defects, and generating inspection reports without requiring manual operator intervention, thereby reducing both time consumption and human error
2Productivity
If existing automated deep learning solutions are used for anomaly detection, then detection speed is improved, but they fail to work for real-time video stream input and require operator confirmation
Solution Approach 1:
The system performs preliminary filtering by pre-processing video frames to identify and eliminate unwanted frames before anomaly detection, preparing the data in advance for real-time processing and enabling fully automated operation without operator confirmation
Solution Approach 2:
The patent segments the video processing pipeline into distinct modules including frame filtering, anomaly detection, and classification, allowing each component to operate independently and efficiently on real-time video streams
3Reliability
If multiple trained models are used for different surface material types, then detection accuracy for varying pipe materials is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal anomaly detection system that can handle multiple surface material types through a common framework with material-specific trained models, allowing the same system architecture to serve diverse inspection needs without requiring separate systems for each material type
Solution Approach 2:
The system adapts to different surface materials by changing the parameters and weights of trained models based on the identified material type, allowing flexible adjustment of detection criteria without altering the fundamental system structure
4Reliability
If all frames from video stream are processed for anomaly detection, then comprehensive coverage is achieved, but processing time increases significantly
Solution Approach 1:
The patent extracts and processes only the relevant frames from the video stream by filtering out unwanted frames that do not contain potential anomalies, reducing the total number of frames requiring detailed analysis while maintaining comprehensive coverage of actual defects
Solution Approach 2:
The system applies partial processing by performing lightweight preliminary analysis on all frames and detailed anomaly detection only on selected frames of interest, avoiding excessive processing of every single frame while ensuring no significant anomalies are missed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables fast and accurate anomaly detection and classification, reducing manual effort and improving accuracy by processing only relevant frames and adapting to changing pipe conditions, thus enhancing the efficiency and reliability of pipe inspection processes.
Implementation Method 1
eliminating one or more unwanted frames from the plurality of frames based on a magnitude of 2-Dimensional optical flow vectors of each of the plurality of frames. The method includes identifying one or more potential anomaly frames from the plurality of frames based on contours in a dense map created using a magnitude of displacement of each pixel of a frame from the plurality of frames
Data Source
AI summary
A method and a system are described for detection of anomalies in surfaces, such as pipes. The method includes receiving an input comprising surface material type and plurality of frames of a real-time video stream associated with the surface. The method includes eliminating unwanted frames based on magnitude of 2-Dimensional optical flow vectors of the plurality of frames. The method includes identifying potential anomaly frames based on contours in a dense map created using a magnitude of displacement of each pixel of a frame. The method includes detecting in real-time anomalies in anomaly frames from potential anomaly frames based on trained models selected from a Model Mapping Table. The method includes classifying anomalies in the anomaly frames into anomaly classes using one or more deep learning techniques. The method includes generating a health report comprising anomalies in anomaly frames associated with the surface and providing health report to a user.