Object recognition in forward-looking sonar images with Convolutional Neural Networks

Valdenegro-Toro, Matías

doi:10.1109/oceans.2016.7761140

Cited by 43 publications

(24 citation statements)

References 8 publications

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“…Furthermore, marine robots are also being exploited to collect optical and acoustic imagery that can be analyzed by a human operator to monitor areas of interest and detect particular patterns that highlight the ongoing physical-chemical processes. Modern Automatic Target Recognition (ATR) strategies, based on Deep Neural Networks (DNN), applied to both optical and acoustic images [56][57][58], represent a possible and promising solution to help (or even replace) human interventions in these particular tasks.…”

Section: Payloadsmentioning

confidence: 99%

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

Ridolfi

Secciani

Stroobant

et al. 2021

JMSE

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Coastal zones are subjected to a wide range of phenomena acting on very different temporal and spatial scales: from decades to days and from hundreds of kilometers to tens of meters. Planning the management of such areas, thus, requires an accurate and updated knowledge of the ongoing processes. While standard monitoring activities are functional for the medium-long time scale and medium-large spatial scale, they struggle to provide adequate information concerning the short period (i.e., days) and small range (i.e., few meters). In addition, such operations are affected by high costs and logistic complexity since they generally involve the deployment of specific aircraft or maritime vehicles. On the contrary, the employment of robotic devices can represent a solution to these issues. Their proper use can allow for frequent surveys and enhance the coverage of the acquired data due to optimized mission strategies. Marine robotics has the potential to arise as an efficient complementary tool to standard monitoring techniques. Nevertheless, the use of marine robots is still limited and should be improved. The purpose of this paper is to discuss the current state of robotic technology, identifying both the benefits and shortcomings of its use for micro-tidal marine-coastal monitoring. The discussion will be supported by actual results, taken as an example, achieved using FeelHippo AUV, the compact Autonomous Underwater Vehicle (AUV) developed by the Department of Industrial Engineering at the University of Florence, Italy.

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Section: Payloadsmentioning

confidence: 99%

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

Ridolfi

Secciani

Stroobant

et al. 2021

JMSE

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“…In [82], CNNs were employed for object detection in forward-looking sonar (FLS) images. They captured FLS images from their water tank, comprising several kinds of debris objects, with an ARIS Explorer 3000, at the 3.0 MHz frequency setting with a variation of 1 to 3 meters.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

2020

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Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

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“…In Valdenegro 21 words: "This key finding signals that deep and convolutional neural networks are a clear direction for future research in sonar image processing". Juhwan et al 22,23 applied CNN to FLS images to localize a remotely operated vehicle (ROV) linked to the AUV through a tether cable. Two different labeled images, the ROV images, and some background images were collected.…”

Section: Cnn Concepts For Fls Imagesmentioning

confidence: 99%

Thoughts on object detection using convolutional neural networks for forward-looking sonar

Livne

Baruch

Guterman

2018

IRATJ

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This work reviews the problem of detection in Forward-Looking Sonar images. In the underwater realm, most of the imaging is done by acoustic means, i.e. sonar. The Forward-Looking Sonar usually has a very low Signal to Noise Ratio therefore object detection in Forward-Looking Sonar images is still an open issue. The article will introduce our database and some conclusions that were gathered from working with it. It will also show results from a Convolutional Neural Network designed for Forward-Looking Sonar Images.

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Object recognition in forward-looking sonar images with Convolutional Neural Networks

Cited by 43 publications

References 8 publications

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Thoughts on object detection using convolutional neural networks for forward-looking sonar

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