Geospatial Object Detection Post-Processing System
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Solution Overview
Problem
State-of-the-art computer vision technologies for identifying objects in geospatial imagery suffer from inaccuracies, including false positives, false negatives, misidentification of boundaries, and attributes, leading to errors and uncertainties.
Innovation Solution
A post-processing system that correlates multiple detections of objects across geospatial images to filter out false detections and verify true ones by associating consistent attributes, improving the accuracy of object identification and attribute assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer vision models are used to identify objects in geospatial imagery, then object detection capability is provided, but accuracy and reliability deteriorate due to false positives, false negatives, and attribute misidentification
Solution Approach 1:
The system implements feedback by using detected objects and their attributes as training data to continuously retrain and improve the computer vision models. The detected objects, verified through consistency checks across multiple images, are fed back into the training pipeline to refine model parameters and reduce future detection errors and attribute misidentifications.
Solution Approach 2:
The system introduces an intermediary verification layer that checks consistency of detected objects and attributes across multiple geospatial images before finalizing identification. This intermediary step acts as a mediator between raw model outputs and final results, filtering out false positives and resolving ambiguities through cross-image validation.
2Reliability
If multiple computer vision models are deployed to improve detection accuracy, then reliability improves, but system complexity and computational resources increase
Solution Approach 1:
The system merges results from multiple computer vision models by detecting the same object type across different models and images, then consolidating detections through consistency verification. Instead of running models independently and combining outputs, the system integrates detection processes and verifies results through cross-validation, reducing redundant computations while maintaining reliability.
Solution Approach 2:
The system employs self-service through automated consistency checking and verification processes that independently validate detections without requiring manual intervention. The models self-correct by comparing detections across multiple images and models, automatically filtering false positives and confirming true detections through internal verification mechanisms.
Data Source
AI summary
Exemplary systems and methods perform post-processing operations on computer vision model detections of objects of interest in geospatial imagery to detect and assign attributes to the detected objects of interest. For example, an exemplary post-processing system correlates multiple detections, made by a computer vision model, of an object of interest depicted within a set of images of a geospatial location, determines, based on the correlated detections, an attribute of the object of interest depicted within the set of images of the geospatial location, and selects the attribute for inclusion in a dataset for the object of interest. Corresponding methods and systems are also disclosed.