UAV Asset Mapping With Real-Time Image Segmentation
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Solution Overview
Problem
Current asset mapping methods are costly, labor-intensive, and prone to errors due to manual processing, making them inefficient and time-consuming.
Innovation Solution
An unmanned aerial vehicle (UAV) equipped with a camera, GPS sensor, and deep learning-based image processing capabilities, utilizing multi-threading architecture for real-time asset detection and mapping, which includes image segmentation and machine learning models to generate associations between detected objects and geographical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual asset mapping is performed by human surveyors, then asset location information can be obtained, but the process is time-consuming, labor-intensive, and error-prone
Solution Approach 1:
The patent replaces manual mechanical surveying operations with an automated UAV-based system equipped with cameras and machine learning algorithms. The UAV autonomously captures images and the system automatically processes them using deep learning models to detect and map assets, eliminating the need for manual image analysis by human surveyors and dramatically reducing both time and human error.
Solution Approach 2:
The system performs self-service through automated image capture by the UAV and autonomous processing using machine learning models. The algorithm independently identifies assets in captured images, determines their locations using GPS data, and updates the map database without requiring manual intervention, thereby achieving both speed and accuracy simultaneously.
2Loss of information
If manual asset mapping is performed, then asset information can be recorded, but it requires huge cost and a lot of manual labor
Solution Approach 1:
The patent replaces complex manual operations with an integrated UAV system that combines automated flight control, image capture, GPS tracking, and machine learning-based asset detection. This automated system reduces operational complexity while ensuring complete asset information is captured through systematic area coverage and automated image processing.
Solution Approach 2:
The UAV system performs multiple functions in a single integrated platform: navigation using GPS, image capture using onboard cameras, automatic image processing using machine learning models, and database updates. This multi-functional approach eliminates the need for separate manual operations for each task, reducing both cost and complexity while maintaining information completeness.
3Area of stationary object
If manual asset mapping is performed with multiple surveyors, then coverage can be increased, but errors increase due to human factors and coordination requirements
Solution Approach 1:
The patent segments the surveyed area into multiple zones that can be covered by the UAV's flight path. The system systematically captures images across the entire area and uses parallel processing with multiple CPU cores to analyze images simultaneously, achieving both extensive coverage and high accuracy without the coordination errors inherent in manual multi-surveyor operations.
Solution Approach 2:
The patent replaces multiple human surveyors with a single UAV system that autonomously covers the entire surveyed area. The machine learning algorithms consistently identify assets with high accuracy across all regions without the variability, fatigue, or coordination errors that plague manual operations, thereby maintaining both comprehensive coverage and high reliability.
Data Source
AI summary
The disclosure provides an unmanned aerial vehicle, a method, and an apparatus for real-time asset mapping. The unmanned aerial vehicle is configured to, for example, control an image sensor to capture an image of a geographical region. Further, the unmanned aerial vehicle is configured to detect a first object of a set of objects in the captured image based on a segmentation of the captured image into one or more segments. The unmanned aerial vehicle is further configured to generate an association between the detected first object and the captured image. Further, the unmanned aerial vehicle is configured to transmit the generated association to a map database.