Aerial Object Detection Using Disparity Mapping and Segmentation
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Solution Overview
Problem
Existing aerial image processing systems are time-consuming, require significant manual input, and lack the ability to improve over time for accurate object identification and estimation.
Innovation Solution
A system utilizing disparity mapping and segmentation techniques, including region growing and split-and-merge algorithms, to automatically detect and classify objects in aerial images, which evolves in efficiency and accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional aerial image processing systems are used, then object identification can be achieved, but the process is time-consuming and requires significant manual input
Solution Approach 1:
The system performs self-improvement by automatically learning from new data and refining its detection algorithms over time without requiring manual reconfiguration or intervention, enabling the system to become progressively more accurate and efficient autonomously
Solution Approach 2:
The patent replaces manual mechanical processing with automated computer-based image processing algorithms, including machine learning and computer vision techniques, to detect and classify objects in aerial images without human intervention
2Reliability
If traditional aerial image processing systems are used, then object detection can be performed, but the systems lack the ability to improve results over time
Solution Approach 1:
The system incorporates feedback mechanisms where detection results are continuously evaluated and used to refine and improve the algorithms, creating a closed-loop system that learns from its performance and progressively enhances accuracy
Solution Approach 2:
The detection system is designed to be dynamic and adaptive, continuously evolving its parameters and algorithms based on new data and experiences, rather than remaining static and fixed
3Measurement precision
If manual processing methods are used, then detailed object analysis can be achieved, but the process becomes difficult to use and requires great deal of manual input
Solution Approach 1:
The system automatically performs detailed object analysis without requiring manual operation, making the complex precision detection tasks execute autonomously while maintaining high accuracy
Data Source
AI summary
A system for aerial image detection and classification is provided herein. The system comprising an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre-processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre-defined elevation threshold.