Underwater Field Equipment of a Network of Landmarks Optimized for Automatic Detection by AI

Beaudoin, Laurent; Avanthey, Loïca

doi:10.1109/igarss39084.2020.9323589

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A mechanism given in [73] dynamically chooses feature layer channels, termed as DC block and is combined with YOLOX to make YOLOX-DC. A network establishment concept with defined local points in underwater environment is given in [74] that uses the YOLO version for automatic target detection and eases the manual measurement in future trials. The YOLO algorithm is modified in [75] and transfer learning is adopted to ease the complexity of training and target detection.…”

Section: E Object Detection Using Yolomentioning

confidence: 99%

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

Khan,

Fouda,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper provides a study of the latest target (object) detection algorithms for underwater wireless sensor networks (UWSNs). To ensure selection of the latest and state-of-the-art algorithms, only algorithms developed in the last seven years are taken into account that are not entirely addressed by the existing surveys. These algorithms are classified based on their architecture and methodologies of operation and their applications are described that are helpful in their selection in a diverse set of applications. The merits and demerits of the algorithms are also addressed that are helpful to improve their performance in future investigation. Moreover, a comparative analysis of the described algorithms is also given that further provides an insight to their selection in various applications and future enhancement. A depiction of the addressed algorithms in various applications based on publication count over the latest decade (2023-2013) is also given using the IEEE database that is helpful in knowing their future application trend. Finally, the challenges associated with the underwater target detection are highlighted and the future research paradigms are identified. The conducted study is helpful in providing a thorough analysis of the underwater target detection algorithms, their feasibility in various applications with future challenges and defined strategies for further investigation. INDEX TERMSUnderwater target detection, deep learning, underwater object detection, YOLO, convolutional neural networks, ConVNNs.I. INTRODUCTION Underwater wireless sensor networks (UWSNs) is one of the latest realms of research that aims to explore the underwater environment for a number of applications. These applications include Tsunami detection and prediction [1], military surveillance [2], underwater navigation [3], secure communications [4], oil detection [5], fault monitoring in underwater cables [6], water quality monitoring [7], detection of crashed ships

show abstract

Section: E Object Detection Using Yolomentioning

confidence: 99%

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

Khan,

Fouda,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Automatically Guided Selection of a Set of Underwater Calibration Images

Beaudoin

Avanthey

Bunel

et al. 2022

JMSE

View full text Add to dashboard Cite

The 3D reconstruction of underwater scenes from overlapping images requires modeling the sensor. While underwater self-calibration gives good results when coupled with multi-view algorithms, calibration or pre-calibration with a pattern is still necessary when scenes are weakly textured or if there are not enough points of view of the same points; however, detecting patterns on underwater images or obtaining a good distribution of these patterns on a dataset is not an easy task. Thus, we propose a methodology to guide the acquisition of a relevant underwater calibration dataset. This process is intended to provide feedback in near real-time to the operator to guide the acquisition and stop it when a sufficient number of relevant calibration images have been reached. To perform this, pattern detection must be optimized both in time and success rate. We propose three variations of optimized detection algorithms, each of which takes into account different hardware capabilities. We present the results obtained on a homemade database composed of 60,000 images taken both in pools and at sea.

show abstract

Underwater Field Equipment of a Network of Landmarks Optimized for Automatic Detection by AI

Cited by 2 publications

References 8 publications

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

Automatically Guided Selection of a Set of Underwater Calibration Images

Contact Info

Product

Resources

About