Drone Imaging Control Using Feedback for Disturbance Adaptation
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Solution Overview
Problem
It is challenging to maintain optimal image-capturing conditions for drones due to disturbances like wind, obstacles, and backlight, which can lead to the need for repeated image capturing or manned monitoring.
Innovation Solution
A control device that analyzes images captured by a drone's image capturing device and adjusts preset image-capturing conditions, such as altitude, speed, and route, to ensure optimal image quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset image-capturing conditions are used for drone operation, then operational simplicity is maintained, but image quality deteriorates under disturbances like wind, obstacles, and backlight
Solution Approach 1:
The system captures images according to preset conditions, analyzes the captured images to evaluate quality metrics (sharpness, exposure, composition), and automatically adjusts subsequent image-capturing conditions based on this analysis feedback. This closed-loop control enables the drone to adapt to disturbances like wind, obstacles, and backlight while maintaining operational simplicity.
Solution Approach 2:
The image-capturing conditions are transformed from static preset values to dynamic parameters that automatically adjust based on real-time image analysis results. The system dynamically modifies shooting parameters such as focal length, aperture, shutter speed, and positioning coordinates to optimize image quality under varying environmental conditions.
2Manufacturing precision
If manual monitoring is performed during image capturing to ensure quality, then image quality improves, but productivity decreases due to increased operational time
Solution Approach 1:
The drone system performs self-monitoring and self-correction of image-capturing conditions through automated image analysis. The onboard analysis unit evaluates captured images in real-time and automatically adjusts shooting parameters without requiring manual intervention, thereby maintaining high image quality while improving operational efficiency.
Solution Approach 2:
The system establishes a real-time feedback loop where captured images are immediately analyzed for quality assessment, and adjustment instructions are automatically generated and applied to subsequent shots. This eliminates the need for manual monitoring while ensuring consistent image quality, thereby resolving the contradiction between quality and productivity.
3Manufacturing precision
If repeated image capturing is performed to compensate for poor quality shots, then image quality improves, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis of image quality metrics before finalizing capture decisions. By evaluating sharpness, exposure, and composition in advance, the system can determine whether a shot meets quality standards and adjust parameters proactively for the next capture, preventing the need for repeated retakes and reducing time loss.
Solution Approach 2:
Real-time image analysis provides immediate feedback on capture quality, enabling the system to make rapid parameter adjustments between shots. This feedback mechanism allows the drone to correct issues like motion blur or poor exposure before they result in unusable images, thereby reducing the frequency of repeated captures and minimizing time loss.
Data Source
AI summary
The present technique relates to a control device and method and a program for enabling image capturing under optimum image-capturing conditions. A control section corrects preset image-capturing conditions on the basis of an analysis result of an image captured by an image capturing device included in a mobile body. A driving control section controls driving of the mobile body on the basis of the corrected image-capturing conditions. The present technique is applicable to a mobile-body control system that controls the mobile body.