Robust Underwater Localization Using Acoustic Image Alignment for Autonomous Intervention Systems

Park, Jisung; Kim, Jinwhan

doi:10.1109/access.2022.3179430

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…With the vast majority of the world's oceans still unexplored, the ability to accurately detect and locate underwater targets such as minerals, oil and gas deposits, and marine life is essential for sustainable and effective resource management. Underwater target detection technology includes a variety of methods, such as sonar and acoustic imaging [1][2][3], magnetic and electromagnetic sensing [4,5], and visual inspection using remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). These technologies allow researchers and industry professionals to map the seafloor, identify potential resource deposits, and locate marine life for conservation or fishing purposes.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

et al. 2023

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Underwater target detection is a critical task in various applications, including environmental monitoring, underwater exploration, and marine resource management. As the demand for underwater observation and exploitation continues to grow, there is a greater need for reliable and efficient methods of detecting underwater targets. However, the unique underwater environment often leads to significant degradation of the image quality, which results in reduced detection accuracy. This paper proposes an improved YOLOv5 underwater-target-detection network to enhance accuracy and reduce missed detection. First, we added the global attention mechanism (GAM) to the backbone network, which could retain the channel and spatial information to a greater extent and strengthen cross-dimensional interaction so as to improve the ability of the backbone network to extract features. Then, we introduced the fusion block based on DAMO-YOLO for the neck, which enhanced the system’s ability to extract features at different scales. Finally, we used the SIoU loss to measure the degree of matching between the target box and the regression box, which accelerated the convergence and improved the accuracy. The results obtained from experiments on the URPC2019 dataset revealed that our model achieved an mAP@0.5 score of 80.2%, representing a 1.8% and 2.3% increase in performance compared to YOLOv7 and YOLOv8, respectively, which means our method achieved state-of-the-art (SOTA) performance. Moreover, additional evaluations on the MS COCO dataset indicated that our model’s mAP@0.5:0.95 reached 51.0%, surpassing advanced methods such as ViDT and RF-Next, demonstrating the versatility of our enhanced model architecture.

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