A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images

Park, Jin‐Hyun; Kang, Changgu

doi:10.3390/jmse8110952

Cited by 23 publications

(16 citation statements)

References 27 publications

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“…Besides the GMM and optical flow, in the object detection task on videos, the detection result of the same object on previous frames will give stronger confidence for detection on the current frame. [63] proposes a technique based on the YOLO framework for properly classifying things and counting their numbers in consecutive underwater video pictures by accumulating object classification results from previous frames to the present frame. The cumulative mean for each object can be computed as follows:…”

Section: Contextual Informationmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

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Underwater marine object detection, as one of the most fundamental techniques in marine engineering, had been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been applied widely in monitoring of underwater ecosystems, exploration of natural resources, management of commercial fishery, and so on. However, due to the complexity of the underwater environment, characteristics of marine objects, and limitations from exploration equipment, the detection performance in terms of speed, accuracy and robustness could be degraded dramatically using conventional approaches. Deep learning algorithms have found significant impact in many fields of engineering and science including marine engineering. In this context, We offer an overview of deep-learning-based underwater marine object detection techniques. We organize the present research challenges into three areas to assist a thorough knowledge of the subject matter, namely image quality degradation, small object detection, and poor generalization. We evaluate recent advances and emphasize the advantages and risks of existing solutions for each category of extant challenges. In addition, we examine the most extensively used benchmark datasets for underwater marine object detection in detail and critically. Comparisons between previous reviews and prospective advancements in the subject field, notably Artificial-Intelligence-based approaches, are also presented.

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Section: Contextual Informationmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides the GMM and optical flow, in the object detection task on videos, the detection result of the same object on previous frames will give stronger confidence for detection on the current frame. In [90], a method based on the YOLO framework is proposed to accurately classify objects, and count their numbers in sequential underwater video images by accumulating the object classification results from the past frames to the current frame. The cumulative mean for each object can be computed as follows:…”

Section: Contextual Informationmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

Underwater marine object detection, as one of the most fundamental techniques in marine engineering, has been shown to exhibit significant potential for exploring underwater environment in recent years. It has been applied widely in the monitoring of underwater ecosystems, exploration of natural resources, management of commercial fishery management, and other areas. However, due to the complexity of underwater environment, characteristics of marine object, and limitation from exploration equipment, the detection performance revolving around speed, accuracy and robustness could be degraded dramatically. In this context, we present a survey of deep-learning-based underwater marine object detection. To facilitate comprehensive understanding of the subject matter, we categorize the existing research challenges of underwater object detection into image quality degradation, small object detection, poor generalization and real-time detection. Corresponding to each category of the existing challenges, we review recent advances and highlight the pros and cons of existing techniques. Furthermore, we give a detailed and critical review of the most widely used benchmark datasets for underwater marine object detection. Comparisons with existing reviews and future trends of the subject matter, particularly AI-based techniques, are also presented.

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“…Context information [39], [40], [41], [42], [43], [44], [45] Super-resolution [46], [47], [48] The balance of positive and negative examples [49], [37], [50], [51], [52] Poor generalization…”

Section: Image Quality Degradationmentioning

confidence: 99%

“…The ConvLSTM model provides a real-world object detection maneuver for underwater object gripping, giving a shred of significant proof concerning effectiveness and efficiency of temporal context information Besides low-level vision features, detection results of the same object in previous frames also give stronger confidence for detection in future frames. In [44], to preserve rare and endangered objects or to eliminate exotic invasive species, a method based on the YOLO framework [84] is proposed for properly classifying objects and counting their numbers in consecutive underwater video images. It accumulates object classification results from previous frames to the present frame.…”

Section: B Detection Of Small Objectsmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the ocean in recent years. It has been widely applied in monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, and so on. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness could be degraded dramatically when conventional approaches are used. Deep learning algorithms have been found to have significant impacts on a variety of engineering fields, including marine engineering. In this context, we offer a review of deep-learning-based underwater marine object detection techniques. To facilitate a thorough understanding of the subject, we organize research challenges of underwater object detection into three categories, namely image quality degradation, small object detection, and poor generalization. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we examine the most extensively used benchmark datasets in detail and critically. Comparative studies with previous reviews, notably approaches that leverage artificial intelligence, and future trends on this hot topic are also presented. * This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images

Cited by 23 publications

References 27 publications

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

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