Operation Detection From Image Frames Using Time-Series Correction
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Solution Overview
Problem
Image recognition using machine learning for operation detection can lead to misrecognition and requires extensive time-series data, making it inefficient for accurate operation detail specification.
Innovation Solution
An information processing apparatus that acquires frames from images, specifies operation details using machine learning, and corrects these details based on time-series relationships and a predetermined operation order, thereby improving accuracy by identifying continuous frames and operation periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If image recognition using machine learning is used for operation detection, then operation details can be automatically detected, but misrecognition occurs and sufficient learning data cannot be collected in time
Solution Approach 1:
The system uses a period specification unit that provides feedback correction to the operation specification unit. When the operation period is specified based on time series relationships and predetermined operation orders, this feedback mechanism allows the system to identify and correct misrecognition errors, thereby improving reliability while maintaining automation.
2Measurement precision
If machine learning takes time series into account for accurate operation detection, then recognition accuracy improves, but processing time increases and sufficient learning data cannot be collected
Solution Approach 1:
The system segments the operation detection process into two independent units: an operation specification unit that handles initial detection and a period specification unit that handles time-series verification. This segmentation allows the system to achieve accurate time-series-based detection without requiring the entire process to be computationally intensive, thereby reducing data collection time while maintaining precision.
3Measurement precision
If operation details are corrected based on time series relationships and operation orders, then accuracy of specifying operation details improves, but processing complexity increases
Solution Approach 1:
The period specification unit acts as an intermediary between the operation specification unit and the final output. It receives operation details, applies time-series relationship verification and operation order checking, and returns corrected results. This intermediary approach improves accuracy while containing complexity within a dedicated module rather than distributing it throughout the entire system.
Data Source
AI summary
An acquisition unit acquires frames constituting an image. An operation specification unit specifies operation details based on the image of the frames by way of machine learning using a learning model. A period specification unit corrects the operation details associated with the image of the frames based on time series relationship between a first period and a second period of periods included in the image and based on a predetermined operation order of a plurality of operation details and to specify a period of each operation corresponding to the operation order, the first period corresponding to continuous frames in which a first operation detail is specified by the operation specification unit, the second period being different from the first period and corresponding to continuous frames in which a second operation detail is specified.