3D-Modeling Dataset Augmentation for Underwater AUV Real-time Manipulations

Wang, Chua-Chin; Huang, Chien-Chih; Lin, Chu-Han; Yeh, Chia‐Hung; Liu, Guanxian; Chou, Yu-Cheng

doi:10.1109/apccas50809.2020.9301679

Cited by 3 publications

(2 citation statements)

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“…Therefore, dataset augmentation methods have become a recurrent strategy to ensure the establishment of the greater image databases that are usually required to train deep neural network algorithms. For instance, a 3D-modeling dataset augmentation method was proposed for AUV real-time operations [ 89 ], increasing the significance of rare underwater objects for detection algorithms. Furthermore, some simulated underwater physical effects were included in a synthetic dataset to build an underwater image-enhancement algorithm for infrastructure inspection [ 90 ].…”

Section: Algorithmic Processingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

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Section: Algorithmic Processingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, the improved algorithm for underwater object detection is more widely studied based on the YOLO series. In 2020, Wang et al [16] utilized the YOLO model to train and predict on a clownfish dataset. By applying 3D rotations and scaling to objects in different backgrounds, the number of underwater images was expanded by over 1000-fold.…”

Section: Introductionmentioning

confidence: 99%

YOLOv6-ESG: A Lightweight Seafood Detection Method

Wang,

Li,

Fang

et al. 2023

JMSE

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The rapid development of convolutional neural networks has significant implications for automated underwater fishing operations. Among these, object detection algorithms based on underwater robots have become a hot topic in both academic and applied research. Due to the complexity of underwater imaging environments, many studies have employed large network structures to enhance the model’s detection accuracy. However, such models contain many parameters and consume substantial memory, making them less suitable for small devices with limited memory and computing capabilities. To address these issues, a YOLOv6-based lightweight underwater object detection model, YOLOv6-ESG, is proposed to detect seafood, such as echinus, holothurian, starfish, and scallop. First, a more lightweight backbone network is designed by rebuilding the EfficientNetv2 with a lightweight ODConv module to reduce the number of parameters and floating-point operations. Then, this study improves the neck layer using lightweight GSConv and VoVGSCSP modules to enhance the network’s ability to detect small objects. Meanwhile, to improve the detection accuracy of small underwater objects with poor image quality and low resolution, the SPD-Conv module is also integrated into the two parts of the model. Finally, the Adan optimizer is utilized to speed up model convergence and further improve detection accuracy. To address the issue of interference objects in the URPC2022 dataset, data cleaning has been conducted, followed by experiments on the cleaned dataset. The proposed model achieves 86.6% mAP while the detection speed (batch size = 1) reaches 50.66 FPS. Compared to YOLOv6, the proposed model not only maintains almost the same level of detection accuracy but also achieves faster detection speed. Moreover, the number of parameters and floating-point operations reaches the minimum levels, with reductions of 75.44% and 79.64%, respectively. These results indicate the feasibility of the proposed model in the application of underwater detection tasks.

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