Leakage Detection via Temporal Image Tensor Convolution
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Solution Overview
Problem
Conventional manual inspection methods for detecting leakages in petrochemical facilities and piping systems are labor-intensive and often fail to detect anomalies in real-time, leading to potential hazards and inefficiencies.
Innovation Solution
A method and system that integrate temporal information by acquiring multiple frames of images, merging them into a multi-dimensional tensor, performing convolution processing to calculate frame differences, and inputting the results into a trained target detection model to enhance the accuracy and reliability of leakage detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple frames of images are processed to integrate temporal information, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the temporal information processing into distinct functional modules: frame difference calculation module that computes differences between adjacent frames, accumulation module that aggregates differences over time, and target detection module that identifies leakage targets. This segmentation allows each module to handle specific computational tasks efficiently, improving detection accuracy while managing computational complexity through modular design.
Solution Approach 2:
The patent transforms temporal information from the time dimension into the channel dimension by stacking multiple frames and their accumulated differences as additional channels in the input tensor. This dimensionality change allows the detection model to process temporal information alongside spatial information in a unified framework, improving detection accuracy without requiring separate temporal processing pathways that would increase complexity.
2Reliability
If real-time monitoring is implemented to detect leakages promptly, then safety reliability is improved, but processing time consumption increases
Solution Approach 1:
The patent performs preliminary calculations of frame differences and accumulates them over time before target detection. By pre-processing the temporal information in this manner, the system prepares enhanced input data that highlights changing patterns, allowing the target detection model to work more efficiently and reduce actual detection processing time while maintaining high safety reliability through continuous monitoring.
3Device complexity
If conventional manual inspection is used to detect leakages, then system complexity is reduced, but detection capability deteriorates
Solution Approach 1:
The patent replaces manual inspection with an automated computer vision system that captures images, calculates frame differences, accumulates temporal information, and detects leakage targets automatically. This substitution eliminates human labor while significantly improving detection capability through continuous automated monitoring and pattern recognition algorithms, accepting increased system complexity as the trade-off for enhanced detection performance.
Data Source
AI summary
A method and system for detecting a leakage target by integrating temporal information. The method comprises: acquiring multiple frames of images of a target position, and arranging the multiple frames of images based on temporal information corresponding to the multiple frames of images to obtain a target image sequence; merging, in a channel dimension, the multiple frames of images in the target image sequence to obtain a multi-dimensional tensor; performing convolution processing on the multi-dimensional tensor to obtain a first output result, wherein the first output result is used for representing a frame difference between every two adjacent ones of the multiple frames of images; calculating an accumulated frame difference corresponding to the multiple frames of images based on the first output result; and inputting the accumulated frame difference to a trained target detection model as input data to obtain a second output result integrated with the temporal information.