Underwater-Drone With Panoramic Camera for Automatic Fish Recognition Based on Deep Learning

Meng, Lin; Hirayama, Takuma; Oyanagi, Shigeru

doi:10.1109/access.2018.2820326

Cited by 120 publications

(59 citation statements)

References 18 publications

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“…After DL model training is complete, such models can show excellent speed for live fish identification purposes. For example, one model required only 6 s to identify 115 images (Meng et al 2018); the average time to detect lionfish in each frame was only 0.097 s (Naddaf-Sh et al 2018). Therefore, under the premise of reasonable accuracy, a DL model's recognition speed can satisfy real-time requirements (Villon et al ).…”

Section: Live Fish Identificationmentioning

confidence: 99%

“…Additionally, some related studies have applied data augmentation techniques to artificially increase the number of training samples. Data augmentation can be used to generate new labelled data from existing labelled data through rotation, translation, transposition and other methods (Xu & Cheng 2017;Meng et al 2018). These additional data can help to improve the overall learning process; such data augmentation is particularly important for training DL models on data sets that contain only small numbers of images (Kamilaris & Prenafeta-Bold u 2018).…”

Section: Datamentioning

confidence: 99%

“…The inputs include public data sets such as the ImageNet data set, the Fish4Knowledge (F4K) data set and the Croatian and Queensland University of Technology (QUT) fish data sets. Other data sets include data collected and produced in the field or obtained through Internet search engines, such as Google (Meng et al 2018;Naddaf-Sh et al 2018).…”

Section: Algorithmsmentioning

confidence: 99%

“…For example, one model required only 6 s to identify 115 images (Meng et al . 2018); the average time to detect lionfish in each frame was only 0.097 s (Naddaf‐Sh et al . 2018).…”

Section: Applications Of Deep Learning In Smart Fish Farmingmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

192

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Live Fish Identificationmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

“…For example, one model required only 6 s to identify 115 images (Meng et al . 2018); the average time to detect lionfish in each frame was only 0.097 s (Naddaf‐Sh et al . 2018).…”

Section: Applications Of Deep Learning In Smart Fish Farmingmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

192

View full text Add to dashboard Cite

show abstract

“…Görüntü işleme alanına dahil edebileceğimiz bir çalışmada; doğal bir göldeki balık türlerini araştırmak için balık türlerini tanımaya odaklanılmıştır ve drone'un gözü görevi gören 360• panoramik kameralı bir sualtı drone geliştirilmiştir. Yapılan çalışmalar neticesinde, neredeyse tüm balık türlerinin Alex-Net ve GoogLeNet ile %85'in üzerinde bir tanıma oranıyla doğruluğu saptanmıştır (Meng, Hirayama ve Oyanagi, 2018) .…”

Section: Teknolojiunclassified

Examples of Current Use of Free Software and Open Source and Case Study

Aşici

Zengin

2021

İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Açık Kaynak ve Özgür Yazılımlar verimlilik, kullanılabilirlik, güvenlik, tasarruf, girişimcilik ve daha birçok açısından oldukça önemlidir. Bu çalışma, açık kaynak ve özgür yazılım başlığı altındaki "özgür yazılım", "açık kaynak yazılım" ve "açık kaynak donanım" kavramları ile ilgili farklı yazarlar tarafından elde edilen çalışmaların incelemesini sunmaktadır. Bu sayede çalışmalar hakkında okuyucuya özet bilgiler sunarak konu ile ilgili okuyucunun ön bilgilenmesini sağlamak ve gerekli olan yol haritasını ortaya koymayı hedeflemektedir. Bu çalışma, genel olarak "Eğitim", "Sağlık", "Bilişim-Teknoloji" ve "Endüstri" alanındaki yayınlar üzerine yoğunlaşmıştır.

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Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

et al. 2021

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A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30-yr horizon of the network. In contrast to NEON's extensive use of automated instruments to collect environmental data, NEON's biodiversity surveys are almost entirely conducted using traditional human-centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long-term costs. In this manuscript, we first review the current status of instrument-based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound-producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground-based measurements for plant biodiversity, and laboratory-based imaging for physical specimens and samples in the NEON biorepository. Through its data science-literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human-driven surveys.

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Underwater-Drone With Panoramic Camera for Automatic Fish Recognition Based on Deep Learning

Cited by 120 publications

References 18 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Examples of Current Use of Free Software and Open Source and Case Study

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

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