Drone Inspection Image Mapping for Complete Asset Defect Coverage
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Solution Overview
Problem
Drones face limitations in onboard processing, storage, and data transfer due to size and weight constraints, requiring manual supervision and multiple site visits for monitoring tasks, especially in remote and hard-to-access areas with unique monitoring requirements like wind turbines and solar panels, where conventional methods are inefficient and prone to incomplete or incorrect data collection.
Innovation Solution
A computer program and system that utilizes a drone manager to receive and tag images with corresponding drone positions, map them to a 3D model, identify omitted areas, and generate a second flight path for complete data capture, employing AI and machine learning for image analysis and site shade prediction to optimize inspection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If drones are used for remote site monitoring, then accessibility to hard-to-reach areas is improved, but onboard processing and storage capabilities are limited due to size and weight constraints
Solution Approach 1:
The patent extracts the heavy processing and storage functions from the drone itself and relocates them to ground-based facilities. The drone retains only lightweight imaging and telemetry capabilities, while all substantial data processing, storage, and analysis are performed externally, eliminating the need for large onboard storage while maintaining full monitoring capability.
Solution Approach 2:
The patent introduces telemetry data and 3D model mapping as intermediary elements that enable efficient data management. By tagging images with precise drone position information and mapping them to 3D models, the system creates a lightweight data structure that requires minimal storage space while preserving complete spatial context for analysis.
2Reliability
If manual supervision is used for drone flight, then control and decision-making are improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent implements preliminary automated actions by pre-generating 3D models of inspection targets and pre-tagging images with telemetry data during flight. This preliminary processing eliminates the need for time-consuming manual analysis after the flight, as the system automatically organizes and maps all captured data before the operator even reviews it.
Solution Approach 2:
The system performs self-service through automated flight path generation and data mapping. The drone autonomously follows pre-planned flight paths and automatically tags and maps captured images to the 3D model, reducing the need for continuous manual supervision while maintaining reliable control when intervention is needed.
3Loss of information
If multiple site visits are conducted to ensure complete data collection, then data completeness is improved, but loss of time and increased operational cost worsen
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically compares captured images against the 3D model to identify omitted portions. This feedback loop generates a completeness assessment that immediately indicates whether all required areas have been captured, eliminating the need for multiple visits by providing real-time verification of data completeness.
Solution Approach 2:
By pre-generating comprehensive 3D models of the inspection target before the drone flight, the system establishes a complete reference framework that allows automatic verification of coverage. This preliminary action enables the system to identify any omitted areas immediately after a single flight, ensuring data completeness without requiring return visits.
4Measurement precision
If high-resolution images are captured across the entire site, then image quality is improved, but use of energy and storage requirements worsen
Solution Approach 1:
The patent applies local quality by capturing high-resolution images only at specific locations identified as requiring detailed inspection, rather than uniformly across the entire site. The system uses the 3D model and automated analysis to determine which specific areas need high-resolution imagery, capturing standard resolution elsewhere, thereby reducing overall energy consumption and storage requirements while maintaining measurement precision where needed.
Data Source
AI summary
A set of images of a three-dimensional (3D) inspection object collected by a drone during execution of a first flight path may be received, along with telemetry data from the drone. A tagged set of images may be stored, with each tagged image being stored together with a corresponding drone position at a corresponding time that the tagged image was captured, as obtained from the telemetry data. A mapping of the set of tagged images to corresponding portions of a 3D model of the 3D inspection object may be executed, based on the telemetry data. Based on the mapping, at least one portion of the 3D inspection object omitted from the set of tagged images may be identified. A second flight path may be generated for the drone that specifies a position of the drone to capture an image of the at least one omitted portion of the 3D inspection object.