Multi-Camera Flying Object Detection With Terrain Exclusion
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Solution Overview
Problem
Current flying object monitoring systems face challenges in accurately detecting unrecognizable flying objects, particularly those with similar characteristics to known features like buildings or mountains, which can lead to false positives and reduced detection accuracy.
Innovation Solution
An image processing device that utilizes multiple cameras mounted on flight vehicles or stationary platforms to generate difference images, excluding known topographical features from the analysis, and identifies flying objects based on shape, movement, and altitude differences, enabling precise detection and classification of unmanned aerial vehicles or birds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to detect flying objects, then detection coverage is improved, but false positives increase due to similar characteristics between flying objects and topographical features
Solution Approach 1:
The patent segments the detection task by separating known topographical features from unknown flying objects. The image processing device divides the monitoring region into regions containing known features (excluded from detection) and regions without known features (subject to detection), thereby preventing false positives while maintaining detection coverage.
Solution Approach 2:
The patent introduces an intermediary database storing information about known topographical features (buildings, mountains, etc.) that acts as a mediator between the captured images and the detection algorithm. By comparing captured images against this intermediary database, the system can distinguish between known static features and unknown moving flying objects, reducing false positives.
2Reliability
If difference images are generated to exclude known features, then false positives are reduced, but detection speed decreases due to additional processing steps
Solution Approach 1:
The patent performs preliminary action by pre-storing information about known topographical features in a database before the actual detection process. This allows the system to quickly reference and exclude known features during real-time operation without performing complex real-time analysis of static features, thereby maintaining detection speed while reducing false positives.
Solution Approach 2:
The patent extracts and removes the analysis of known topographical features from the real-time detection process by excluding regions containing known features from difference image generation. This extraction principle separates the processing of known static features from unknown moving objects, reducing computational load while maintaining reliability.
3Measurement precision
If difference image processing is applied to all regions, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by differentiating the processing applied to different regions of the monitoring area. Regions containing known topographical features receive exclusion treatment (no difference image processing), while regions without known features receive full difference image processing. This localized approach maintains detection accuracy where needed while reducing overall computational complexity.
Data Source
AI summary
There is provided an image processing device including: an image acquisition unit for acquiring a first image including, as a subject, a first region captured by a first camera which captures an image from a first altitude toward a direction of an altitude lower than the first altitude, and a second image including, as a subject, the first region captured by a second camera which captures an image from a second altitude toward a direction of an altitude lower than the second altitude; and a flying object detection unit for detecting a flying object at an altitude lower than the first altitude and the second altitude, based on a difference between the first region in the first image and the first region in the second image.