Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

Reviews in Aquaculture, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 1, 2025

ABSTRACT Digital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional practices. This article presents a comprehensive review of three interconnected digital tasks, namely, fish tracking, counting, behaviour analysis, using novel unified approach. Unlike previous reviews which focused on single modalities or individual we analyse vision‐based (i.e., image‐ video‐based), acoustic‐based, biosensor‐based methods across all tasks. We examine their advantages, limitations, applications, highlighting recent advancements identifying critical cross‐cutting research gaps. The also includes emerging ideas such as applying multitask learning large language models to address various aspects monitoring, an approach not previously explored in literature. identify the major obstacles hindering progress this field, including scarcity datasets lack evaluation standards. To overcome current explore potential multimodal data fusion deep improve accuracy, robustness, efficiency integrated monitoring systems. In addition, provide summary existing available for analysis. holistic perspective offers roadmap future research, emphasizing need standards facilitate meaningful comparisons between promote practical implementations real‐world settings.

Язык: Английский

---

ConvFishNet: An efficient backbone for fish classification from composited underwater images

Information Sciences, Год журнала: 2024, Номер 679, С. 121078 - 121078

Опубликована: Июнь 19, 2024

Язык: Английский

---

Automated fish counting system based on instance segmentation in aquaculture

Expert Systems with Applications, Год журнала: 2024, Номер 259, С. 125318 - 125318

Опубликована: Сен. 7, 2024

Язык: Английский

---

Semi-supervised and weakly-supervised deep neural networks and dataset for fish detection in turbid underwater videos

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102303 - 102303

Опубликована: Сен. 11, 2023

Fish are key members of marine ecosystems, and they have a significant share in the healthy human diet. Besides, fish abundance is an excellent indicator water quality, as adapted to various levels oxygen, turbidity, nutrients, pH. To detect underwater videos, Deep Neural Networks (DNNs) can be great assistance. However, training DNNs highly dependent on large, labeled datasets, while labeling turbid video frames laborious time-consuming task, hindering development accurate efficient models for detection. address this problem, firstly, we collected dataset called FishInTurbidWater, which consists collection footage gathered from waters, quickly weakly (i.e., giving higher priority speed over accuracy) them 4-times fast-forwarding software. Next, designed implemented semi-supervised contrastive learning detection model that self-supervised using unlabeled data, then fine-tuned with small fraction (20%) our FishInTurbidWater data. At next step, trained, novel weakly-supervised ensemble DNN transfer ImageNet. The results show leads more than 20 times faster turnaround time between result generation, reasonably high accuracy (89%). same time, proposed waters (94%) accuracy, still cutting by factor four, compared fully-supervised trained carefully datasets. Our code publicly available at hyperlink FishInTurbidWater.

Язык: Английский

---

Charting the aquaculture internet of things impact: Key applications, challenges, and future trend

Aquaculture Reports, Год журнала: 2024, Номер 39, С. 102358 - 102358

Опубликована: Сен. 20, 2024

Язык: Английский

---

A data-centric framework for combating domain shift in underwater object detection with image enhancement

Applied Intelligence, Год журнала: 2025, Номер 55(4)

Опубликована: Янв. 4, 2025

Abstract Underwater object detection has numerous applications in protecting, exploring, and exploiting aquatic environments. However, underwater environments pose a unique set of challenges for including variable turbidity, colour casts, light conditions. These phenomena represent domain shift need to be accounted during design evaluation models. Although methods have been extensively studied, most proposed approaches do not address inherent In this work we propose data-centric framework combating with image enhancement. We show that there is significant gap accuracy popular detectors when tested their ability generalize new domains. used our compare 14 processing enhancement efficacy improve generalization using three diverse real-world datasets two widely algorithms. Using an independent test set, approach superseded the mean average precision performance existing model-centric by 1.7–8.0 percentage points. summary, demonstrated contribution generalization.

Язык: Английский

---

Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand

Marine Pollution Bulletin, Год журнала: 2025, Номер 214, С. 117710 - 117710

Опубликована: Фев. 20, 2025

Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices complex environmental conditions in marine ecosystems. In response these challenges, this field study conducted Koh Tao, Thailand, proposed an innovative cost-effective approach that leverages super-resolution reconstruction (SRR) technology conjunction optimized object model based on YOLOv8. Super-resolution (SR) images reconstructed by seven SRR models were fed into Seafloor-Debris-YOLO (SFD-YOLO) for detection. RDN achieved highest results a signal-to-noise ratio (PSNR) 41.02 dB structural similarity (SSIM) 95.08 % attained state-of-the-art (SOTA) accuracy mean Average Precision (mAP) 91.2 using RDN-reconstructed magnification factor 4. Additionally, provided in-depth analysis influence factors within process, offering quantitative comparison before after reconstruction, as well comparative evaluation across various algorithms novel pretraining strategy. This survey methods, combined technology, marks advancement monitoring, presenting practical solutions enhance image quality affected enabling precise identification debris.

Язык: Английский

---

CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints

Information, Год журнала: 2025, Номер 16(2), С. 154 - 154

Опубликована: Фев. 19, 2025

Automated fish species classification is essential for marine biodiversity monitoring, fisheries management, and ecological research. However, challenges such as environmental variability, class imbalance, computational demands hinder the development of robust models. This study investigates effectiveness convolutional neural network (CNN)-based models hybrid approaches to address these challenges. Eight CNN architectures, including DenseNet121, MobileNetV2, Xception, were compared alongside traditional classifiers like support vector machines (SVMs) random forest. DenseNet121 achieved highest accuracy (90.2%), leveraging its superior feature extraction generalization capabilities, while MobileNetV2 balanced (83.57%) with efficiency, processing images in 0.07 s, making it ideal real-time deployment. Advanced preprocessing techniques, data augmentation, turbidity simulation, transfer learning, employed enhance dataset robustness imbalance. Hybrid combining CNNs intermediate improved interpretability. Optimization pruning quantization, reduced model size by 73.7%, enabling deployment on resource-constrained devices. Grad-CAM visualizations further enhanced interpretability identifying key image regions influencing predictions. highlights potential CNN-based scalable, interpretable classification, offering actionable insights sustainable management conservation.

Язык: Английский

---

Employing an innovative underwater camera to improve electronic monitoring in the commercial Gulf of Mexico reef fish fishery

PLoS ONE, Год журнала: 2024, Номер 19(3), С. e0298588 - e0298588

Опубликована: Март 8, 2024

Vessel electronic monitoring (EM) systems used in fisheries around the world apply a variety of cameras to record catch as it is brought on deck and during fish processing activities. In EM work conducted by Center for Fisheries Electronic Monitoring at Mote (CFEMM) Gulf Mexico commercial reef fishery, there was need improve upon current technologies enhance camera views accurate species identification large sharks, particularly those that were released while underwater vessel side or underneath hull. This paper describes how this problem addressed with development first known system integrated (UCAM) specialized vessel-specific deployment device bottom longline vessel. Data are presented based blind video reviews from CFEMM trained reviewers resulting UCAM footage compared only overhead 68 gear retrievals collected eight fishing trips. Results revealed successful tool capturing clear (>2m) sharks enable individual identification, determination, fate 34.4%. important obtaining data incidental catches protected shark species. It also provided imagery presence potential predators such marine mammals close vessel, more specifically bottlenose dolphin ( Tursiops truncatus ) retrieval, which often damaged removed catch. information intended assist researchers gathering critical bycatch proximity conventional limited.

Язык: Английский

---

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 35, С. 101205 - 101205

Опубликована: Апрель 21, 2024

Язык: Английский

---