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We believe our results point in a direction that Neural Networks combined with FLS form a powerful object detection system for small objects in underwater environments, which is ideal for marine debris.
The experimental results demonstrate the array's ability to detect the velocity of underwater objects towed past by with high accuracy, and an average error of only 2.5%.
Object detection in underwater unconstrained environments is useful in domains like marine biology and geology, where the scientists need to study fish populations, underwater geological events etc.
The results of the testing suggest that this device could be used to detect underwater sounds in various applications.
This pipeline is suitable for detecting underwater objects in practice by our experiences.
This design has potential applications in underwater communication and underwater detection.
The experimental result proves that the vehicle is applicable to the underwater detection.
The results show that it has good stability and antinoise ability for multiple underwater objects localizations.
The result of the real experiment demonstrated that the proposed framework can automatically perform 3D measurement tasks of underwater objects.

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