A double sided deep nonnegative matrix factorization network for feature extraction of two dimensional data

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126652 - 126652

Published: Jan. 1, 2025

Language: Английский

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Overcoming annotation bottlenecks in underwater fish segmentation: a robust self-supervised learning approach

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(4)

Published: Feb. 4, 2025

Language: Английский

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Automatic detection, identification and counting of deep-water snappers on underwater baited video using deep learning

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 6, 2025

Deep-sea demersal fisheries in the Pacific have strong commercial, cultural, and recreational value, especially snappers (Lutjanidae) which make bulk of catches. Yet, managing these is challenging due to scarcity data. Stereo-Baited Remote Underwater Video Stations (BRUVS) can provide valuable quantitative information on fish stocks, but manually processing large amounts videos time-consuming sometimes unrealistic. To address this issue, we used a Region-based Convolutional Neural Network (Faster R-CNN), deep learning architecture automatically detect, identify count deep-water BRUVS. Videos were collected New Caledonia (South Pacific) at depths ranging from 47 552 m. Using dataset 12,100 annotations 11 snapper species observed 6,364 images, obtained good model performance for 6 with sufficient (F-measures >0.7, up 0.87). The correlation between automatic manual estimates MaxN abundance was high (0.72 – 0.9), Faster R-CNN showed an underestimation bias higher abundances. A semi-automatic protocol where our supported observers BRUVS footage improved 0.96 counts perfect match (R=1) some key species. This already assist semi-automatically process will certainly improve when more training data be available decrease rate false negatives. study further shows that use artificial intelligence marine science progressive warranted future.

Language: Английский

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A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1022 - 1022

Published: Feb. 9, 2025

Recent advances in fish transportation technologies and deep machine learning-based classification have created an opportunity for real-time, autonomous sorting through a selective passage mechanism. This research presents case study of novel application that utilizes learning to detect partially dewatered exiting Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images volitionally passing ASFL. Then, this integrated with network video recorder monitor from the Additional models were also using similar scanning device test feasibility approach classification. Open source software edge computing design principles employed ensure system is capable fast data processing. The findings demonstrate such ASFL can support real-time detection. contributes goal automated collection viable path towards realizing optical sorting.

Language: Английский

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Real-time detection of hypoxic stress behavior in aquaculture fish using an enhanced YOLOv8 model

Aquaculture International, Journal Year: 2025, Volume and Issue: 33(3)

Published: Feb. 25, 2025

Language: Английский

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