Critical object recognition in underwater environment

Nunes, Alexandra; Gaspar, Ana Rita; Matos, Aníbal

doi:10.1109/oceanse.2019.8867360

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Rekik et al [ 37 ] developed the first trainable system to detect underwater pipelines, extracting several features and using a Support Vector Machine to classify between positive and negative underwater pipe image samples. Convolutional Neural Networks were introduced by Nunes et al [ 38 ] to classify diverse underwater objects, including a pipeline. None of these works determined the position of the object within the image, only a binary classification of the object’s presence was given.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

Martin‐Abadal

Oliver-Codina

González-Cid

2022

Sensors

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Nowadays, more frequently, it is necessary to perform underwater operations such as surveying an area or inspecting and intervening on industrial infrastructures such as offshore oil and gas rigs or pipeline networks. Recently, the use of Autonomous Underwater Vehicles (AUV) has grown as a way to automate these tasks, reducing risks and execution time. One of the used sensing modalities is vision, providing RGB high-quality information in the mid to low range, making it appropriate for manipulation or detail inspection tasks. This work presents the use of a deep neural network to perform pixel-wise 3D segmentation of pipes and valves on underwater point clouds generated using a stereo pair of cameras. In addition, two novel algorithms are built to extract information from the detected instances, providing pipe vectors, gripping points, the position of structural elements such as elbows or connections, and valve type and orientation. The information extracted on spatially referenced point clouds can be unified to form an information map of an inspected area. Results show outstanding performance on the network segmentation task, achieving a mean F1-score value of 88.0% at a pixel-wise level and of 95.3% at an instance level. The information extraction algorithm also showcased excellent metrics when extracting information from pipe instances and their structural elements and good enough metrics when extracting data from valves. Finally, the neural network and information algorithms are implemented on an AUV and executed in real-time, validating that the output information stream frame rate of 0.72 fps is high enough to perform manipulation tasks and to ensure full seabed coverage during inspection tasks. The used dataset, along with a trained model and the information algorithms, are provided to the scientific community.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

Martin‐Abadal

Oliver-Codina

González-Cid

2022

Sensors

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“…Later, Nunes et. al introduced the application of a Convolutional Neural Network in [38] to classify up to five underwater objects, including a pipeline. In both of these works, no position of the object is given, but simply a binary output on the object's presence.…”

Section: State Of the Artmentioning

confidence: 99%

Underwater Pipe and Valve 3D Recognition Using Deep Learning Segmentation

Martin‐Abadal

Piñar-Molina

Martorell-Torres

et al. 2020

JMSE

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During the past few decades, the need to intervene in underwater scenarios has grown due to the increasing necessity to perform tasks like underwater infrastructure inspection and maintenance or archaeology and geology exploration. In the last few years, the usage of Autonomous Underwater Vehicles (AUVs) has eased the workload and risks of such interventions. To automate these tasks, the AUVs have to gather the information of their surroundings, interpret it and make decisions based on it. The two main perception modalities used at close range are laser and video. In this paper, we propose the usage of a deep neural network to recognise pipes and valves in multiple underwater scenarios, using 3D RGB point cloud information provided by a stereo camera. We generate a diverse and rich dataset for the network training and testing, assessing the effect of a broad selection of hyperparameters and values. Results show F1-scores of up to 97.2% for a test set containing images with similar characteristics to the training set and up to 89.3% for a secondary test set containing images taken at different environments and with distinct characteristics from the training set. This work demonstrates the validity and robust training of the PointNet neural in underwater scenarios and its applicability for AUV intervention tasks.

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Critical object recognition in underwater environment

Cited by 2 publications

References 3 publications

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

Underwater Pipe and Valve 3D Recognition Using Deep Learning Segmentation

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