Maritime Visual Event Scoring Under Bandwidth Constraints
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Solution Overview
Problem
Existing maritime vessel operations face challenges in efficiently assessing and communicating visual data and events due to bandwidth limitations and the need for manual surveys, which are time-consuming and costly, hindering effective risk management and compliance with safety and regulatory standards.
Innovation Solution
An automated system and method for real-time, near-real-time, and subsequent visual evidence-based reporting and assessment of maritime vessel risk, utilizing cameras and processors to detect maritime visual events, generate risk assessment scores, and aggregate data across fleets, employing machine learning and deep learning techniques to analyze compliance with standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual paper surveys are used to collect vessel information, then comprehensive risk assessment data can be obtained, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical survey processes with an automated computer vision system using cameras and machine learning algorithms to detect and analyze maritime events, transforming visual data into risk assessment scores automatically without human intervention in data collection
Solution Approach 2:
The system enables self-assessment by automatically detecting events, comparing them against compliance standards, generating risk scores, and producing reports without requiring external surveyors, allowing the vessel operations to assess their own risk profile continuously
2Reliability
If visual sensors are deployed to monitor vessel operations, then real-time risk detection is improved, but bandwidth limitations hinder data transmission
Solution Approach 1:
The system extracts only the essential risk assessment scores and key event data from the visual information stream for transmission, rather than sending all raw visual data, thereby reducing bandwidth requirements while maintaining monitoring effectiveness
Solution Approach 2:
The monitoring system is segmented into onboard processing that handles full visual analysis and shore-based systems that receive summarized risk data, allowing real-time local detection with minimal communication overhead
3Speed
If automated event detection is implemented, then response time to maritime events is reduced, but system complexity increases
Solution Approach 1:
The system uses a universal machine learning model that can detect multiple types of maritime events (safety violations, security threats, maintenance issues, cargo problems) across different vessel types and locations, reducing the need for specialized detection systems for each scenario
Data Source
AI summary
A system and method assessing maritime vessel risk in response to automatically detected maritime visual events is provided. At least one maritime visual event is detected by at least one camera aboard a vessel that provides image data of the visual event to a processor. The visual event can be associated with at least one of, safety, security, maintenance, crew behavior, and cargo. A risk assessment score is produced in response to the detected visual event, and that risk assessment score is provided to a user in a desired format. Production of the risk assessment score can entail comparing the visual event to data of complying or non-complying model visual events from a data storage. Risk assessment scores can be aggregated from plurality of events and/or a fleet of vessels to generate overall scores for the vessel and fleet.