Automated Stroboscope Detection via Optical Flow Analysis
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Solution Overview
Problem
Conventional stroboscope systems are manual, leading to inefficiencies in inspecting fast-moving objects, and they lack the automation and real-time capabilities needed for precise industrial applications. Additionally, they are limited in detecting irregular or complex failure modes and require skilled operators for accurate interpretation.
Innovation Solution
A stroboscopic device equipped with a camera, light source, and hardware processors, utilizing software to generate synthetic video frames and detect the stroboscopic effect through optical flow analysis, enabling automated detection and analysis of motion patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual stroboscope inspection procedures are used, then operators can visually inspect fast-moving objects, but the system under inspection must be prevented from operation until inspection is complete, resulting in loss of time and productivity
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical measurement system that captures images of fast-moving objects and uses image processing algorithms to detect defects. This substitution eliminates the need to stop the inspected system, as the automated system can analyze moving objects in real-time, thereby maintaining productivity while ensuring inspection accuracy.
Solution Approach 2:
The patent enables continuous inspection by capturing multiple images of the same moving object at different positions during its motion cycle. The system processes these images continuously without interrupting the object's movement or the production line, allowing inspection to occur during normal operation rather than requiring system shutdown.
2Speed
If conventional stroboscopes are used to visualize motion, then high-frequency movements can be perceived, but the systems are limited to rotating or reciprocating machinery with repetitive motion patterns
Solution Approach 1:
The patent develops a universal inspection system that can handle various types of moving objects including rotating machinery, reciprocating components, conveyor belts, and other non-repetitive motion patterns. The system uses general-purpose image capture and processing algorithms that adapt to different motion types, making it applicable across diverse equipment rather than being limited to specific machinery types.
Solution Approach 2:
The patent employs dynamic image processing techniques that can track and analyze objects with varying motion patterns. The system captures images at multiple timestamps and uses optical flow analysis to handle both repetitive and irregular motion, allowing it to adapt to different equipment types and failure modes without requiring specialized stroboscope settings for each case.
3Measurement precision
If conventional stroboscopes are used for visual inspection, then motion can be frozen or slowed, but skilled operators are required to interpret visual data correctly, leading to subjectivity and potential errors
Solution Approach 1:
The patent implements an automated image processing system that performs defect detection and analysis without human intervention. The system automatically captures images, processes them using algorithms, identifies defects, and generates inspection results. This eliminates the need for skilled operators to interpret visual data manually, removing subjectivity and potential human error while maintaining inspection precision.
Solution Approach 2:
The patent replaces the human operator's visual interpretation capability with automated computer vision algorithms. The system uses image processing techniques to objectively analyze captured images, detect defects, and generate quantitative results, substituting human subjectivity with machine-based objective measurement and reducing the complexity of operator skill requirements.
4Loss of information
If conventional stroboscopes provide visual data, then motion patterns can be observed, but detailed quantitative data is lacking compared to vibration analysis or thermal imaging
Solution Approach 1:
The patent transitions from qualitative visual observation to quantitative measurement by extracting numerical data from images. The system measures parameters such as position, velocity, acceleration, and defect dimensions directly from captured images, adding a quantitative dimension to the inspection data. This enables precise measurement capabilities comparable to vibration analysis while maintaining the visual inspection approach.
Solution Approach 2:
The patent introduces image processing algorithms as an intermediary between the visual data captured by the camera and the final inspection results. These algorithms extract quantitative information from the images, such as object position, motion characteristics, and defect measurements, converting visual information into precise numerical data that can be used for analysis and decision-making.
5Extent of automation
If automated processing is performed on the cloud, then computational tasks can be executed remotely, but it increases the time to complete operations and creates latency issues for real-time stroboscope inspection
Solution Approach 1:
The patent divides the processing system into local and cloud components. Critical real-time processing tasks such as image capture, initial defect detection, and stroboscope control are performed locally on the device to minimize latency. Non-critical tasks such as data storage, advanced analysis, and report generation are offloaded to the cloud. This segmentation allows automated processing while maintaining real-time performance for critical functions.
Solution Approach 2:
The patent performs preliminary processing of images and data locally before transmitting to the cloud. The system captures images, performs initial defect detection, and prepares data in advance, so that when data is transmitted to the cloud for further processing, the critical time-sensitive operations have already been completed locally, minimizing the impact of cloud processing latency on real-time inspection.
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
The system achieves robust and accurate detection of stroboscopic effects even in less-than-ideal conditions, providing real-time operation with reduced latency and enabling the automatic detection of movement or vibration of objects.
Implementation Method 1
a camera for acquiring video frames
Implementation Method 2
a light source
Implementation Method 3
detect the stroboscopic effect through optical flow analysis
Data Source
AI summary
A stroboscopic device, comprising: a camera for acquiring video frames; a light source; and at least one hardware processor; and software that is configured to, when executed by the at least one hardware processor, generating synthetic video frames of an object, providing real video frames of the object and the synthetic video frames to a discriminator configured to detect the difference between the real video frames from the synthetic video frames, using feedback from the discriminator to generate further synthetic video frames, providing real video frames of the object and the further synthetic video frames to the discriminator and repeating until a convergence point is achieved where the discriminator can't reliably tell the real video frames from the synthetically generated video frames, generating a data set comprising synthetic video frames and real vide frames of the object, using the data set to train a model, acquire a sequence of video frames via the camera, for a first and second frame in the sequence of video frames, compute the dense optical flow field between the two frames, wherein the optical flow field contains a flow vector for each pixel in each of the first and second frames, indicating the motion of that pixel from the first frame to the second frame, calculate the average magnitude (Average Optical Flow or AOF) of all flow vectors for each pixel in the first and second frames, compare the AOF to a threshold, and store a result based on the comparison, repeat the process for all frames in the sequence of video frames, determine whether a stroboscopic effect has been achieved based on the store results, control the activation of the light source until the results indicate that the stroboscopic effect has been achieved, and obtain a further sequence of video frames after the stroboscopic effect is achieved, identify the object in the further sequence of video frames using the model, and automatically detect movement or vibration of the identified object using the model.