Shark-EYE: A Deep Inference Convolutional Neural Network of Shark Detection for Underwater Diving Surveillance

Merencilla, Nino E.; Alon, Alvin Sarraga; Fernando, Glenn John O.; Cepe, Elaine M.; Malunao, Dennis C.

doi:10.1109/iccike51210.2021.9410715

Cited by 16 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, detecting marine creatures has also become favorable (X. Liu [22], Merencilla et al [23], Li et al [24]). Shark-EYE was developed by Merencilla [23] by using the YOLOv3 algorithm to detect single shark fish, including multi-scale prediction and bounding box prediction-based logistic regression. They achieved mAP values up to 86%.…”

Section: Deep Learning-basedmentioning

confidence: 99%

Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

Khai

Abdullah

Hasan

et al. 2022

Water

View full text Add to dashboard Cite

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.

show abstract