UAV Frameout Detection via Positional Feedback
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Solution Overview
Problem
Existing unmanned aerial vehicles (UAVs) face challenges in determining whether an object has moved out of the image capturing range during inspection, leading to frameout issues, which are difficult to detect without reviewing captured video data, increasing inspection costs and complexity.
Innovation Solution
An UAV system equipped with an image capturing unit, detection unit, angle control unit, determination unit, and storage unit to assess if an object is out of the image capturing range, with the ability to store image capture failure information and control re-capture operations based on this data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UAV autonomously follows the object to be inspected using object detection sensor data, then the UAV can maintain a constant positional relationship and avoid contact with the object, but the object may still move out of the image capturing range due to sudden attitude changes from external factors like strong wind
Solution Approach 1:
The determination unit performs preliminary assessment of whether the object is within the image capturing range by comparing detection unit data with image capturing unit parameters before actual image capture occurs. This allows the system to predict potential frameout issues and take corrective action in advance, improving image capture reliability without adding complex real-time intervention mechanisms.
Solution Approach 2:
The system establishes a feedback loop where the determination unit continuously monitors the relationship between object position (from detection unit) and image capturing parameters, and provides feedback to the flight control unit. When frameout is detected or predicted, the feedback triggers automatic re-capture operations, ensuring reliable image capture while maintaining system simplicity through rule-based control.
2Measurement precision
If the UAV continuously monitors and adjusts to keep the object in the image capturing range, then image capture quality improves, but the response time to detect and correct frameout situations increases
Solution Approach 1:
The determination unit performs preliminary assessment of frameout status by comparing detection unit data with image capturing parameters before actual image capture. This advance detection allows the system to correct positioning issues before they result in missed captures, improving detection precision without adding time loss since the assessment occurs in parallel with normal flight operations.
Solution Approach 2:
The system performs partial monitoring by the determination unit assessing frameout risk based on detection unit data and image capturing parameters, rather than continuously analyzing full video streams. This partial action approach maintains high detection precision for critical frameout conditions while minimizing time loss through selective, efficient calculations.
3Measurement precision
If the system performs manual review of captured video data to determine whether frameout occurred, then accurate detection of capture failures is achieved, but inspection costs and complexity significantly increase
Solution Approach 1:
The determination unit enables the system to self-assess whether frameout occurred by automatically comparing detection unit object position data with image capturing unit parameters. This self-service mechanism eliminates the need for manual video review, maintaining high frameout detection accuracy while significantly reducing inspection process complexity and costs.
Solution Approach 2:
The system replaces the mechanical process of manual video review with an automated determination unit that uses detection unit data and image capturing parameters to assess frameout status. This substitution maintains accurate frameout detection while eliminating the complexity and costs associated with manual inspection processes.
4Reliability
If the UAV performs re-capture operations whenever frameout is detected, then image capture completeness improves, but flight efficiency and productivity decrease
Solution Approach 1:
The determination unit provides feedback on frameout detection to the flight control unit, which then decides whether re-capture is necessary. This feedback mechanism ensures complete image capture by triggering re-capture only when frameout is actually detected or predicted, avoiding unnecessary re-capture operations that would reduce productivity while maintaining capture completeness.
Solution Approach 2:
The system performs partial re-capture operations only for segments where frameout is detected by the determination unit, rather than re-capturing entire inspection routes. This selective approach maintains image capture completeness for affected areas while preserving overall inspection productivity by avoiding redundant re-capture of already successfully captured segments.
Data Source
AI summary
An unmanned aerial vehicle includes an imaging camera configured to capture the image of an object to be inspected, an object detecting unit configured to detect a relative position of the object to be inspected, a camera angle adjusting unit configured to control the angle of an image capturing direction of the imaging camera on the basis of the relative position detected by the object detecting unit, a determination unit configured to determine whether the image of the object to be inspected is moved out of a video frame of the imaging camera during an image capture operation performed by the imaging camera, and a storage unit configured to store image capture failure information including the result of determination if the determination unit determines that the image of the object is moved out of the video frame.