Drone Opening Detection Using Neural Network Segmentation
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Solution Overview
Problem
Current drone navigation systems face challenges in accurately detecting and tracking openings such as windows and doors, especially in complex indoor environments, limiting their ability to autonomously navigate through obstacles and perform precise tasks.
Innovation Solution
The development of a method and system using neural networking techniques for real-time detection and tracking of openings, employing semantic segmentation, edge detection, and corner detection to identify and follow potential passages, allowing for precise bounding box computation and tracking between image frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current drone navigation systems are used, then basic flight control is maintained, but accurate detection and tracking of openings in complex indoor environments is limited
Solution Approach 1:
The system segments the image processing task into multiple specialized modules: semantic segmentation to identify passage regions, edge detection to outline boundaries, and corner detection to locate key vertices. This segmentation allows each module to optimize for its specific function, improving overall detection accuracy in complex environments
Solution Approach 2:
The neural network system performs multiple functions within a unified framework: it simultaneously conducts semantic segmentation, edge detection, corner extraction, and tracking across sequential frames. This multi-functional approach enables the system to adapt to various indoor environments while maintaining consistent performance
2Extent of automation
If real-time detection and tracking is implemented, then autonomous navigation capability is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying passage regions through semantic segmentation before conducting more detailed edge and corner analysis. Corner detection is performed preliminarily to establish tracking points before full passage tracking begins, reducing real-time computational burden while maintaining autonomous navigation capability
Solution Approach 2:
The system transitions from 2D image analysis to 3D spatial understanding by extracting corner points that define the geometric structure of passages. This dimensional transformation enables the system to infer three-dimensional passage geometry from two-dimensional images, improving autonomous navigation without proportionally increasing computational complexity
3Measurement precision
If multiple detection methods (semantic segmentation, edge detection, corner detection) are combined, then passage detection precision is improved, but processing time increases
Solution Approach 1:
The detection process is segmented into hierarchical stages: semantic segmentation first identifies candidate passage regions, then edge detection refines boundaries within those regions, and finally corner detection locates key points. This segmentation allows the system to apply computationally intensive methods only where needed, reducing overall processing time while maintaining high precision
Solution Approach 2:
The system applies detection methods selectively rather than uniformly across the entire image. Semantic segmentation identifies only relevant passage regions, and subsequent edge and corner detection is applied only to these segmented regions. This partial action approach maintains high detection precision while significantly reducing processing time compared to applying all methods to the full image
4Measurement precision
If tracking is performed between sequential frames, then passage tracking accuracy is improved, but computational load increases
Solution Approach 1:
The system uses corner points as simplified copies or representations of entire passages for tracking purposes. Instead of processing full passage images across frames, the system tracks only the extracted corner points, which serve as lightweight proxies that maintain tracking accuracy while dramatically reducing computational energy consumption
Solution Approach 2:
The system extracts only the essential tracking information (corner points and key edges) from full passage images, separating the critical tracking data from the complete visual information. This extraction allows the system to perform tracking with minimal computational load while maintaining accuracy by focusing on the most informative features
Data Source
AI summary
In some embodiments, the present application relates to methods and systems for real-time detection and tracking of potential passages in an environment, including a) detecting one or more passages in one or more frames of image data; b) extracting one or more corners for each of the one or more detected passages; c) tracking one or more points between frames of image data for each of the one or more detected passages in one or more frames of image data; and d) assigning one or more passages detected in a frame of image data to one or more previously-detected passages in a different frame of image data.