Above-Horizon Target Tracking with Horizon and Cloud Segmentation
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Solution Overview
Problem
Conventional methods for above-horizon target tracking face challenges in accurately detecting and tracking objects beyond the horizon due to interference from data such as clouds and ground features, posing safety risks for aircraft and personnel.
Innovation Solution
A system comprising an imaging sensor, an imaging module, an image-processing module, and a tracking module, which captures images, processes them to identify objects above the horizon, and tracks their trajectory to determine positions relative to a robot, such as an aircraft, using algorithms like Otsu thresholding and morphological operations to segment and highlight features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for above-horizon target tracking, then the system can operate with simple processing, but detection accuracy deteriorates due to interference from clouds and ground data
Solution Approach 1:
The patent segments the imaging data into distinct components: horizon line detection separates the sky region from ground data, cloud detection identifies atmospheric interference, and target detection isolates objects of interest. This segmentation allows the system to process only relevant portions of the data, improving detection accuracy while managing complexity through modular processing stages.
Solution Approach 2:
The patent extracts and removes interfering elements from the imaging data by detecting and masking ground data below the horizon line and identifying cloud regions. By taking out these harmful factors before target detection, the system improves measurement precision without requiring overly complex processing of the entire scene.
2Productivity
If the system processes all captured image data, then complete scene information is available, but processing time increases due to unnecessary ground and cloud data
Solution Approach 1:
The patent divides the image processing task into segmented stages: horizon detection first identifies the sky-ground boundary, then cloud detection processes only the sky region, and finally target detection operates on cloud-filtered data. This segmentation enables the system to process information efficiently by focusing computational resources on relevant regions, maintaining information completeness for targets while reducing processing time.
Solution Approach 2:
The patent performs preliminary actions by detecting the horizon line and identifying cloud regions before conducting target detection. This preliminary processing filters out irrelevant ground data and atmospheric interference in advance, so that the main target detection algorithm operates on pre-processed, relevant data only, improving processing speed without losing target information.
3Reliability
If the system tracks objects above the horizon, then collision avoidance capability is improved, but false detections from ground objects increase
Solution Approach 1:
The patent extracts and removes ground data from the analysis by detecting the horizon line and masking everything below it. This extraction eliminates the source of false detections from ground objects while preserving all above-horizon targets, directly improving collision avoidance reliability by ensuring only relevant aerial objects are tracked.
Solution Approach 2:
The patent converts the harmful effect of complex environmental data (clouds and ground features) into a benefit by using horizon detection and cloud identification algorithms. These algorithms transform the challenging mixed data into structured information about valid tracking regions, reducing false detections while maintaining comprehensive target awareness for collision avoidance.
Data Source
AI summary
Disclosed herein is a system that comprises an imaging sensor coupled to a robot. The system also comprises an imaging module configured to capture images of a scene within a field of view of the imaging sensor, which is coupled to the robot. The system further comprises an image-processing module configured to process the captured images to identify an object above a horizon in the scene within the field of view of the imaging sensor. The system additionally comprises a tracking module configured to track a trajectory of the object based on the captured images to determine positions of the object relative to the robot.