A General Underwater Object Detector (GUOD) should perform well on most of underwater circumstances. However, with limited underwater dataset, conventional object detection methods suffer from domain shift severely. This paper aims to build a GUOD with small underwater dataset with limited types of water quality. First, we propose a data augmentation method Water Quality Transfer (WQT) to increase domain diversity of the original small dataset. Second, for mining the semantic information from data generated by WQT, DG-YOLO is proposed, which consists of three parts: YOLOv3, DIM and IRM penalty. Finally, experiments on original and synthetic URPC2019 dataset prove that WQT+DG-YOLO achieves promising performance of domain generalization in underwater object detection.
Adaptive Enhancement and Dual-Pooling Sequential Attention for Lightweight Underwater Object Detection with YOLOv10
1,2Department of Computer Science；Engineering, Dhaka University of Engineering \& Technology Gazipur, 1707, Bangladesh 3Department of Computer Science；Engineering, BRAC University Dhaka, Bangladesh
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）
This study examines the effectiveness of spatio-temporal modeling and the integration of spatial attention mechanisms in deep learning models for underwater object detection. Specifically, in the first phase, the performance of temporal-enhanced YOLOv5 variant T-YOLOv5 is evaluated, in comparison with the standard YOLOv5. For the second phase, an augmented version of T-YOLOv5 is developed, through the addition of a Convolutional Block Attention Module (CBAM). By examining the effectiveness of the already pre-existing YOLOv5 and T-YOLOv5 models and of the newly developed T-YOLOv5 with CBAM. Wit…
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
School of Tropical Agriculture and Forestry, Hainan University（海南大学热带农林学院）；School of Information and Communication Engineering（信息与通信工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring
Cadi Ayyad University（卡迪·阿雅德大学）；University of La Réunion（留尼汪大学）；IRD（法国国家可持续发展研究所）；IFREMER（法国海洋开发研究院）；CNRS（法国国家科学研究中心）；University of New Caledonia（新喀里多尼亚大学）；Institut de Recherche pour le Développement（法国国家可持续发展研究所）；MNHN（法国国家自然历史博物馆）；EPHE（巴黎高等研究实践学院）
Jiangsu Ocean University（江苏海洋大学）；Beijing KnowYou Technology Co., Ltd.（北京可优科技有限公司）；Suzhou Institute of Trade & Commerce（苏州经贸职业技术学院）；ShanghaiTech University（上海科技大学）
Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3x3 convolution block in the E-ELAN structure, and incorporates jump connections and 1x1 convolution architecture between ACmixBlock modules …
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection
School of Data Science and Engineering, and Xingzhi College, South China Normal University（数据科学与工程学院和行知学院，华南师范大学）；School of Physics, South China Normal University（物理学院，华南师范大学）；School of Electronics and Information Engineering, and Xingzhi College, South China Normal University（电子与信息工程学院和行知学院，华南师范大学）
As the treasure house of nature, the ocean contains abundant resources. But the coral reefs, which are crucial to the sustainable development of marine life, are facing a huge crisis because of the existence of COTS and other organisms. The protection of society through manual labor is limited and inefficient. The unpredictable nature of the marine environment also makes manual operations risky. The use of robots for underwater operations has become a trend. However, the underwater image acquisition has defects such as weak light, low resolution, and many interferences, while the existing targ…
Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
Faculty of Computing, Sri Lanka Institute of Information Technology（计算学院，斯里兰卡信息科技学院）；Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通信科学学院，塔尔皮埃大学）；Computing Centre, Faculty of Engineering, University of Peradeniya（工程学院计算机中心，珀德尼亚大学）
Deteksi Sampah di Permukaan dan Dalam Perairan pada Objek Video dengan Metode Robust and Efficient Post-Processing dan Tubelet-Level Bounding Box Linking
Clean energy from oceans and rivers is becoming a reality with the development of new technologies like tidal and instream turbines that generate electricity from naturally flowing water. These new technologies are being monitored for effects on fish and other wildlife using underwater video. Methods for automated analysis of underwater video are needed to lower the costs of analysis and improve accuracy. A deep learning model, YOLO, was trained to recognize fish in underwater video using three very different datasets recorded at real-world water power sites. Training and testing with examples…
Northeastern University（东北大学）；Warner Music Group（华纳音乐集团）
For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to the low quality of underwater images and the characteristics of underwater biological, a lack of abundant features may impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environment. Therefore, the goal of this paper is to perform object detection in the underwater environment. This paper proposed a novel method for capturing feature information,…
Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network
University of Agder (UiA)（阿格德大学）；Institute of Marine Research (IMR)（海洋研究所）
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in a…
FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture
Dalian Key Laboratory of Smart Fishery（大连智能渔业重点实验室）；College of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；Key Laboratory of Environment Controlled Aquaculture（环境可控养殖重点实验室）；Dalian Polytechnic University School of International Education（大连职业技术学院国际教育学院）
ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics
Maryland Robotics Center, University of Maryland（马里兰大学马里兰机器人中心）；Center for Autonomous and Robotic Systems, University of Delaware（特拉华大学自主与机器人系统中心）；Automation and Control Institute, TU Wien（维也纳技术大学自动化与控制研究所）；School of Marine Science and Policy, University of Delaware（特拉华大学海洋科学与政策学院）；University of Maryland Center for Environmental Science（马里兰大学环境科学中心）；College of Engineering and Applied Science, University of Cincinnati（辛辛那提大学工程与应用科学学院）；Indepedendent Robotics（独立机器人公司）
