Multi-Modal Sensor Annotation for Computer Vision
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Solution Overview
Problem
Current methods for annotating images or video files in computer vision applications are time-consuming and computationally expensive, requiring intense human effort and processing power, especially when dealing with large datasets from moving imaging devices like unmanned aerial vehicles.
Innovation Solution
The use of multi-modal sensor data, where calibrated sensors like digital cameras and thermographic cameras capture synchronized images, allowing attributes from one modality to enhance the detection and annotation accuracy in another modality, such as transposing thermal attributes onto visual data to improve object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used, then annotation accuracy can be maintained, but annotation time and labor costs increase significantly
Solution Approach 1:
The annotation process is segmented into multiple stages: initial automatic annotation using computer vision algorithms, followed by selective manual verification and refinement. This division allows the system to leverage automated speed while maintaining accuracy through targeted human intervention only where needed.
Solution Approach 2:
A semi-automatic annotation system acts as an intermediary between fully automatic and fully manual methods. The system uses computer vision algorithms to generate preliminary annotations, then presents them to human annotators for verification and correction, combining the speed of automation with the accuracy of manual review.
2Productivity
If automatic annotation methods are used, then annotation speed increases, but computational resources and processing power requirements increase significantly
Solution Approach 1:
Instead of applying computationally intensive automatic annotation to every single image, the system applies automatic annotation selectively based on confidence thresholds and image characteristics. Low-confidence or complex images are routed to manual annotation, optimizing the balance between speed and resource consumption.
Solution Approach 2:
The system dynamically adjusts annotation processing parameters based on image characteristics, complexity, and confidence scores. This allows the computational resources to be allocated efficiently, applying heavy processing only where necessary and using lighter processing for straightforward cases.
3Quantity of substance
If traditional annotation methods are used for large datasets, then complete coverage can be achieved, but the process becomes prohibitively expensive and time-consuming
Solution Approach 1:
The system uses automatically generated annotations from computer vision algorithms to annotate the majority of the dataset without human intervention. This self-service approach handles routine annotation tasks autonomously, reserving human annotators for edge cases and quality assurance, thereby achieving complete dataset coverage at scale.
Solution Approach 2:
The system performs preliminary automatic annotation on the entire dataset before manual review. This preliminary action pre-processes the data, creating a baseline annotation set that can be quickly refined later, rather than starting from scratch with manual annotation for each image.
Data Source
AI summary
Imaging data or other data captured using a camera may be classified based on data captured using another sensor that is calibrated with the camera and operates in a different modality. Where a digital camera configured to capture visual images is calibrated with another sensor such as a thermal camera, a radiographic camera or an ultraviolet camera, and such sensors capture data simultaneously from a scene, the respectively captured data may be processed to detect one or more objects therein. A probability that data depicts one or more objects of interest may be enhanced based on data captured from calibrated sensors operating in different modalities. Where an object of interest is detected to a sufficient degree of confidence, annotated data from which the object was detected may be used to train one or more classifiers to recognize the object, or similar objects, or for any other purpose.