Automated Navigational Marker Detection Using Neural Networks
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Solution Overview
Problem
Traditional maritime navigation methods rely on manual observation of navigational markers, which are prone to human error, especially in adverse weather conditions or at night. Existing automated systems often lack real-time updating and verification of marker positions, leading to potential navigational errors.
Innovation Solution
An automated maritime navigation system that uses RGB cameras, a neural network-based object detector, and a GPS mapping and chart data integration module to detect, identify, and associate navigational markers in real-time, projecting their positions into a 3D coordinate system and cross-referencing them with pre-existing chart data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual observation methods are used for navigational markers, then the system complexity remains low, but the reliability and accuracy of navigation deteriorate due to human error
Solution Approach 1:
The patent replaces manual mechanical observation with an automated computer vision system using RGB cameras and neural network-based object detectors to identify navigational markers, eliminating human error while maintaining manageable system complexity through software-based automation
Solution Approach 2:
The system performs self-verification by cross-referencing detected marker positions with pre-existing chart data, allowing the navigation system to automatically validate and update its own accuracy without external intervention, thereby improving reliability
2Measurement precision
If traditional radar and sonar systems are used, then the device complexity remains moderate, but the measurement precision of navigational markers deteriorates in congested waterways
Solution Approach 1:
The patent substitutes traditional radar and sonar electromagnetic/acoustic detection systems with optical-based computer vision technology using RGB cameras and neural networks, achieving superior precision in detecting navigational markers in congested waterways through image-based recognition
Solution Approach 2:
The system changes the detection parameter from electromagnetic/radar signals to visual optical parameters captured by RGB cameras, enabling precise identification of marker colors, shapes, and positions that are invisible or ambiguous to traditional radar and sonar systems
3Reliability
If electronic chart data is used without real-time updating, then the system complexity remains low, but the reliability of navigational information deteriorates due to outdated marker positions
Solution Approach 1:
The patent implements a feedback mechanism where the computer vision system continuously detects current marker positions, compares them with pre-existing chart data, and updates the electronic navigational charts in real-time, ensuring the reliability of navigational information through continuous verification and correction
Solution Approach 2:
The system transitions from static, pre-loaded electronic chart data to a dynamic updating mechanism where marker positions are continuously tracked and verified against real-time visual detection, allowing the navigational information to adapt and remain accurate as vessels and markers move
Data Source
AI summary
A method for automated navigational marker detection and association in maritime applications includes providing a camera system equipped with one or more red-green-blue (RGB) cameras for capturing visual data of a surrounding maritime environment in real-time, and then providing a computing unit comprising a neural network-based object detector, a projection mechanism module, and a GPS mapping and chart data integration module. Next, providing a database comprising pre-existing chart data of navigational markers. Next, capturing visual data of the surrounding maritime environment using the camera system. Next, processing the visual data by the computing unit using the neural network-based object detector to identify navigational markers. Next, projecting pixel positions of detected navigational markers into a three-dimensional (3D) coordinate system. Next, integrating the projected positions of the detected navigational markers into a navigational map, and then cross-referencing the projected positions of the detected navigational markers with pre-existing chart data of navigational markers for the same location to enhance navigational accuracy and reliability.