UAV Image Precipitation Removal for Autonomous Navigation
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face difficulties in using images for autonomous flight in rainy or snowy conditions due to partial occlusion of objects, leading to degraded accuracy in navigational and safety systems, as well as in building 3D models of their environment.
Innovation Solution
The implementation of image processing techniques that detect and remove precipitation from captured images using stereo disparity, noise pattern, frame difference, and stereo disparity based detection schemes, generating reconstructed images for improved object detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images are used for autonomous flight in rainy or snowy conditions, then the UAV can navigate and build 3D models, but the precipitation occludes objects and degrades accuracy of navigational and safety systems
Solution Approach 1:
The patent extracts and removes precipitation elements from captured images through image processing techniques. The system identifies precipitation particles (raindrops, snowflakes) and removes them from the image data, leaving only the clean background scene. This extraction approach eliminates the harmful occlusion effect while preserving the underlying environmental features needed for navigation and 3D modeling.
Solution Approach 2:
The patent introduces an intermediary image processing system between the camera and the autonomous flight algorithms. This intermediary layer processes the raw images to remove precipitation, providing clean image data to the navigation and object detection systems. The intermediary acts as a mediator that translates degraded weather-affected images into clear, usable visual information.
2Loss of information
If rain or snow is present in the environment, then it can be detected in image data, but the noise caused by precipitation is compounded in 3D models, degrading accuracy
Solution Approach 1:
The patent extracts precipitation elements from image sequences before they can be incorporated into 3D models. By removing raindrops and snowflakes from the image data at the processing stage, the system prevents these transient objects from being reconstructed as permanent features in the 3D environment model, thereby maintaining model accuracy.
Solution Approach 2:
The patent applies precipitation removal as a preliminary processing step before the images are used for 3D model construction. By cleaning the image data in advance, the system ensures that only valid environmental features are captured and reconstructed, preventing noise accumulation in the cumulative 3D model building process.
3Measurement precision
If image processing techniques are applied to remove precipitation, then object detection accuracy improves, but processing complexity and computational load increase
Solution Approach 1:
The patent replaces complex manual or mechanical image processing methods with automated computational algorithms. The system uses computer vision techniques and machine learning models to automatically detect, classify, and remove precipitation elements, substituting sophisticated software processing for simpler but less effective traditional methods.
Solution Approach 2:
The patent implements self-service precipitation removal where the system automatically detects and processes precipitation without requiring manual intervention. The image processing algorithms autonomously identify precipitation patterns, apply appropriate removal techniques, and output cleaned images, making the complex processing transparent to the user.
Data Source
AI summary
Images captured by a camera system of an unmanned aerial vehicle (UAV) can be used to determine a weather condition in an environment of the UAV. The camera system of the UAV can capture one or more images, and a characteristic of at least one image of the one or more images can be determined from image data associated with the at least one image. A weather condition of the environment of the UAV can be determined based at least in part on the characteristic of the at least one image.