USV Proximity Recognition Using Multi-Image Collision Risk Estimation
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Solution Overview
Problem
Unmanned surface vehicles (USVs) face challenges in navigating through unpredictable sea conditions and reducing collision risks due to limited visibility and large inertial forces, which require advanced situational awareness systems.
Innovation Solution
A multi-image-based vessel proximity situation recognition system that uses multiple cameras and navigation sensors on USVs to detect obstacles and provide remote situational awareness of collision risks, utilizing image analysis, navigation data, and fuzzy inference for collision risk estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras and sensors are used to improve situational awareness, then collision detection capability is improved, but device complexity increases
Solution Approach 1:
The system divides the monitoring task into multiple specialized sensors (cameras, LIDAR, radar, AIS receivers) positioned at different locations on the USV. Each sensor handles specific detection tasks, and their data is processed separately before being integrated by the collision risk estimation unit, allowing improved detection capability while managing complexity through functional segmentation
Solution Approach 2:
The system merges data from multiple heterogeneous sensors (visual cameras, LIDAR point clouds, radar signals, AIS communications) into a unified collision risk assessment. The estimation unit integrates these diverse data streams to produce comprehensive situational awareness, achieving reliable collision detection by combining complementary sensor information
2Reliability
If real-time monitoring and tracking are performed to improve collision avoidance, then safety is improved, but information processing requirements increase
Solution Approach 1:
The system performs preliminary processing of sensor data to identify and track potential collision risks before they become immediate threats. The detection unit continuously monitors and tracks objects in advance, allowing the estimation unit to calculate collision risks proactively, which improves safety while distributing information processing load over time rather than requiring peak processing power at critical moments
Solution Approach 2:
The system implements continuous feedback loops where detection results feed into tracking, which feeds into collision risk estimation, which then informs updated detection priorities. This closed-loop information flow optimizes processing by focusing computational resources on high-risk targets identified through feedback from previous analysis cycles, improving safety while managing information processing requirements
Data Source
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AI summary
Proposed is a system and method for multi-image-based vessel proximity situation recognition support. The system for multi-image-based vessel proximity situation recognition support includes an unmanned surface vehicle (USV) configured to detect and track surrounding objects by monitoring surroundings using surrounding images and navigation sensors, and a remote navigation control device configured to support proximity situation recognition of the unmanned surface vehicle according to detection of the surrounding objects, wherein the unmanned surface vehicle may include an image acquisition unit, a navigation sensor unit, and a detection unit.