Camera-Based Neural Networks for Object Identification in Water
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Solution Overview
Problem
Existing systems fail to reliably detect and identify objects partially or fully submerged in water, such as whales or debris, due to limitations in sonar, radar, and lidar technologies, which can lead to collisions with boats.
Innovation Solution
A system using cameras and artificial neural networks to generate and process images of water areas, allowing detection and identification of submerged objects by comparing extracted features with predefined sets, and optionally incorporating inertial measurement for compensation and horizon detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sonar-based technologies are used to detect immersed objects, then detection capability for underwater objects is improved, but detection capability for floating objects is worsened
Solution Approach 1:
The system combines multiple detection technologies (sonar for underwater objects, radar/lidar for floating objects, and optical cameras for identification) into a single integrated detection system, enabling it to detect both immersed and floating objects effectively
2Length of stationary object
If radar systems are used to detect floating objects, then detection range is improved, but identification capability is worsened
Solution Approach 1:
The system uses radar as an intermediary detection layer that provides early warning of floating objects at long ranges, then directs optical cameras to focus on identified targets for precise classification and identification at closer distances
3Measurement precision
If lidar systems are used to detect floating objects, then identification capability is improved, but detection range is worsened
Solution Approach 1:
The system applies lidar selectively only to regions of interest identified by radar or sonar, rather than scanning the entire water area, thereby achieving high identification precision while maintaining efficient use of resources and extended effective detection range
4Reliability
If signals are used to detect floating objects, then detection capability is improved, but signal reliability is worsened
Solution Approach 1:
The system uses optical cameras to capture visual images of detected objects, creating a visual copy that can be analyzed and identified without relying on the potentially lossy or modified signal reflections from the object
Data Source
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AI summary
The invention relates to a system (30) for identifying at least one object (5) at least partially immerged in a water area (3), said system (30) comprising a capturing module (310) comprising at least one camera, said at least one camera being configured to generate at least one sequence of images of said water area (3), and a processing module (320) being configured to receive at least one sequence of images from said at least one camera and comprising at least one artificial neural network, said at least one artificial neural network being configured to detect at least one object (5) in said at least one received sequence of images, extract a set of features from said at least one detected object (5), compare said extracted set of features with at least one predetermined set of features associated with a predefined object (5), identify the at least one detected object (5) when the extracted set of features matches with the at least one predetermined set of features.