Multispectral Element Detection With Cross-Band Classification Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting threats in military environments, such as those posed by dismounted combatants, ground or air vehicles, and drones, are limited by the crew's field of vision, camouflage, and fatigue, leading to potential threats being overlooked.
Innovation Solution
A method and device for detecting elements in an environment using panoramic and high-resolution images, combined with classifiers and motion detectors, to enhance threat detection by re-evaluating classifications based on multiple image resolutions and spectral bands, with confidence-based updates to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis systems are used to detect elements in histology slides, then productivity and consistency are improved, but the ability to detect rare or unexpected findings is reduced
Solution Approach 1:
The system segments the detection task into two distinct stages: an automated analysis phase for routine elements and a manual review phase for flagged cases. This segmentation allows each component to be optimized for its specific function, maintaining high throughput while preserving human expertise for rare findings.
Solution Approach 2:
The system introduces an intermediary flagging mechanism that identifies potential rare findings for further review. This intermediary layer bridges automated efficiency and human expertise, ensuring that rare or unexpected elements are not missed while maintaining overall productivity.
2Reliability
If multiple reviewers are used to improve detection reliability, then detection accuracy is improved, but productivity decreases
Solution Approach 1:
Instead of requiring full manual review of all cases, the system applies partial review only to flagged cases. This partial action approach maintains high reliability for critical detections while preserving productivity by avoiding redundant review of clear-cut cases.
Solution Approach 2:
The system implements feedback loops where review outcomes are used to refine automated detection algorithms. This continuous improvement reduces the need for extensive manual review over time, balancing reliability and productivity dynamically.
3Loss of time
If automated systems are trained on limited datasets, then training time and resources are reduced, but detection precision for diverse cases is worsened
Solution Approach 1:
The system performs preliminary automated detection to identify and flag potential rare findings before they are added to the training dataset. This preliminary action allows the system to accumulate diverse training data efficiently without requiring extensive upfront training on all possible cases.
Solution Approach 2:
The system automatically identifies and collects diverse training cases from its own operation, using flagged findings to self-improve its detection capabilities. This self-service approach to data collection expands training diversity without requiring external resources or time investment.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a method for assisting in the detection of elements in an environment, the method comprising the steps of: - simultaneously acquiring a first image, in a first spectral band, and a second image in a second spectral band, of the same portion of the environment, - detecting and classifying elements imaged in the first image using a classifier which is trained as a function of a first database of images, - detecting and classifying elements imaged in the second image using a classifier which is trained as a function of a second database of images, - comparing the classifications obtained and - when the classification of at least one of the elements detected is different or when an element has been detected only for one of the two images, storing the first and the second images and the corresponding classifications.