UAV Inspection Mission Modification via AI Feedback
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Solution Overview
Problem
Manual and automated inspection methods for structural defects, such as cracks in bridges and buildings, are costly, time-consuming, error-prone, and often produce low-quality images that hinder accurate defect detection and localization.
Innovation Solution
A system utilizing an unmanned aerial vehicle (UAV) platform that receives target asset information, generates inspection missions, transmits UAV-specific commands, receives images and sensor data, and sends this data to an AI services module for feedback, allowing for dynamic modification of inspection missions to ensure high-quality image capture and accurate defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used, then inspectors can visually examine structures, but the process is costly, time-consuming, and error-prone
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated UAV-based inspection system equipped with cameras and sensors. The system captures images and data automatically during flight, eliminating the need for human inspectors to physically examine structures, thereby reducing inspection time while maintaining or improving accuracy through consistent automated data collection.
Solution Approach 2:
The inspection system performs self-service by autonomously navigating to target structures, capturing inspection data, and processing images through AI algorithms. The UAV independently executes inspection missions without continuous human intervention, and the AI services automatically analyze captured images to identify defects, reducing reliance on manual inspection processes.
2Productivity
If automated inspection methods are used, then inspection speed increases, but image quality is low and hinders accurate defect detection
Solution Approach 1:
The system implements feedback by processing captured images through AI services that analyze image quality and provide guidance on re-capturing images if quality is insufficient. The AI services evaluate whether images meet predefined quality thresholds and request additional captures when necessary, ensuring high-quality images are obtained while maintaining automated inspection speed.
Solution Approach 2:
The system performs preliminary actions by pre-defining inspection parameters, flight paths, and image quality thresholds before execution. The AI services are pre-configured with defect detection algorithms and quality criteria, enabling the system to automatically assess and ensure image quality during inspection without manual intervention, thus maintaining both speed and precision.
3Measurement precision
If multiple inspection passes are conducted to improve image quality, then defect detection accuracy improves, but inspection time increases
Solution Approach 1:
The AI services provide real-time feedback on image quality during the inspection process, immediately indicating when images meet quality thresholds. This allows the system to determine whether additional passes are necessary based on actual image quality rather than predetermined schedules, optimizing the balance between detection accuracy and inspection time by conducting re-captures only when quality is insufficient.
Data Source
AI summary
An example system includes a processor to receive target asset information from an asset management system. The processor can generate an inspection mission based on the target asset information. The processor can generate unmanned aerial vehicle (UAV)-specific commands based on the inspection mission. The processor can transmit the UAV-specific commands to an unmanned aerial vehicle (UAV) platform. The processor can receive images and sensor data from the UAV. The processor can also send the images and sensor data to an artificial intelligence (AI) services module. The processor can receive feedback from the AI services module. The processor can further modify the inspection mission based on the feedback.