360-Degree Marine Object Classification Using CNNs
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Solution Overview
Problem
Existing marine object detection systems face challenges in accurately classifying objects in real-time due to limited sensor installation points, processing constraints, and the inability to analyze data from all sides of a vessel, leading to potential incorrect or unnecessary actions in open water navigation.
Innovation Solution
The integration of convolutional neural networks (CNNs) with 360-degree imaging systems for open water object detection, using a rotating camera to capture high-resolution images and employing marine video analytics to detect regions of interest, allowing for accurate classification and real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vessel-based sensors are used for object detection, then real-time detection capability is provided, but the detection coverage is limited due to sensor installation constraints
Solution Approach 1:
The system segments the detection task by using multiple sensors positioned at different locations and orientations on the vessel. Each sensor covers a specific sector, and the system integrates data from all segments to achieve complete 360-degree coverage, resolving the limitation of individual sensor positions.
Solution Approach 2:
The system implements a multi-functional detection architecture where a single integrated processing system handles data from various sensor types (radar, sonar, optical cameras) and processes it uniformly for object detection, classification, and tracking, enabling comprehensive coverage despite diverse sensor installation constraints.
2Measurement precision
If high-resolution 360-degree images are captured for object classification, then classification accuracy is improved, but processing time and computational requirements increase
Solution Approach 1:
The system extracts only the essential features needed for classification from the full 360-degree images using CNNs. Instead of processing entire high-resolution images, the network extracts key visual features and patterns, maintaining classification accuracy while dramatically reducing processing time and computational load.
Solution Approach 2:
The system performs preliminary processing of 360-degree images to identify regions of interest before detailed classification. By pre-processing to detect potential objects and their locations, the system prepares data in advance for faster, more accurate classification without requiring full-image analysis at high resolution.
3Loss of information
If vessel-based processing systems analyze all sensor data, then comprehensive object analysis is achieved, but processing limitations reduce decision-making accuracy
Solution Approach 1:
The system applies different processing strategies to different portions of sensor data based on their characteristics and importance. Critical data from key sensors and regions receives more intensive analysis, while less critical data undergoes lighter processing, optimizing the balance between comprehensive analysis and decision accuracy within processing limitations.
Data Source
AI summary
Marine object detection, localization and classification systems and related techniques include an imaging system configured capture a stream of panoramic images of the water surrounding a mobile structure, including a view of the horizon. The images may include a 360-degree view from the mobile structure. The system is configured to analyze the stream of images using a marine video analytics system and/or a convolutional neural network to detect a region of interest comprising an object on the surface of the water, classify the detected object and relay the results to the user and/or a processing system. The analysis may include determining a horizon in a captured image, defining tiles across the horizon, and detecting objects in each tile.