WYKRYWANIE I KLASYFIKACJA RYB W CZASIE RZECZYWISTYM W ŚRODOWISKU PODWODNYM PRZY UŻYCIU YOLOV5: BADANIE PORÓWNAWCZE ARCHITEKTUR GŁĘBOKIEGO UCZENIA

Rizki Multajam


Universiti Malaysia Terengganu (Malezja)

Ahmad Faisal Mohamad Ayob


Universiti Malaysia Terengganu (Malezja)

W.S. Mada Sanjaya


Universitas Islam Negeri Sunan Gunung Djati (Indonezja)

Aceng Sambas


Universiti Sultan Zainal Abidin (Malezja)

Volodymyr Rusyn

rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information (Ukraina)
https://orcid.org/0000-0001-6219-1031

Andrii Samila


Yuriy Fedkovych Chernivtsi National University (Ukraina)

Abstrakt

Niniejszy artykuł bada metody wykrywania i klasyfikacji ryb jako integralną część podwodnych systemów monitorowania środowiska. Wykorzystując innowacyjne podejście, badania koncentrują się na opracowaniu metod w czasie rzeczywistym do bardzo dokładnego wykrywania i klasyfikacji ryb. Wprowadzenie zaawansowanych technologii, takich jak YOLO (You Only Look Once) V5, stanowi podstawę wydajnego i responsywnego systemu. Badanie ocenia również różne podejścia w kontekście głębokiego uczenia się, aby porównać wydajność i dokładność wykrywania i klasyfikacji ryb. Oczekuje się, że wyniki tych badań przyczynią się do rozwoju bardziej zaawansowanych i wydajnych systemów monitorowania zbiorników wodnych w celu zrozumienia podwodnych ekosystemów i wysiłków na rzecz ochrony przyrody.


Słowa kluczowe:

deep learning, YOLOv5, metody czasu rzeczywistego, ONNX, automatyczne wykrywanie i klasyfikacja ryb

[1] Abdul Aziz M. F. et al.: Development of Smart Sorting Machine using artificial intelligence for Chili Fertigation Industries. Journal of Automation, Mobile Robotics and Intelligent Systems 28, 2022, 44–52 [https://doi.org/10.14313/jamris/4-2021/26].
DOI: https://doi.org/10.14313/JAMRIS/4-2021/26   Google Scholar

[2] Ayob A. et al.: Analysis of pruned neural networks (mobilenetv2-yolo v2) for underwater object detection. 11th National Technical Seminar on Unmanned System Technology 2019 NUSYS’19, Springer Singapore, Singapore, 2021, 87–98.
DOI: https://doi.org/10.1007/978-981-15-5281-6_7   Google Scholar

[3] Boudhane M., Benayad N.: Underwater Image Processing Method for Fish Localization and Detection in Submarine Environment. Journal of Visual Communication and Image Representation 39, 2016, 226–238 [https://doi.org/10.1016/j.jvcir.2016.05.017].
DOI: https://doi.org/10.1016/j.jvcir.2016.05.017   Google Scholar

[4] Brownscombe J. W. et al.: The Future of Recreational Fisheries: Advances in Science, Monitoring, Management, and Practice. Fisheries Research 211, 2019, 247–255 [https://doi.org/10.1016/j.fishres.2018.10.019].
DOI: https://doi.org/10.1016/j.fishres.2018.10.019   Google Scholar

[5] Chen PH. C. et al.: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration for Cancer Diagnosis. Nature Medicine 25(9), 2019, 1453–1457 [https://doi.org/10.1038/s41591-019-0539-7].
DOI: https://doi.org/10.1038/s41591-019-0539-7   Google Scholar

[6] Du J.: Understanding of Object Detection Based on CNN Family and YOLO. Journal of Physics: Conference Series 1004, 2018, 012029 [https://doi.org/10.1088/1742-6596/1004/1/012029].
DOI: https://doi.org/10.1088/1742-6596/1004/1/012029   Google Scholar

[7] Fan F.-L. et al.: On Interpretability of Artificial Neural Networks: A Survey. IEEE Transactions on Radiation and Plasma Medical Sciences 5(6), 2021, 741–760 [https://doi.org/10.1109/trpms.2021.3066428].
DOI: https://doi.org/10.1109/TRPMS.2021.3066428   Google Scholar

[8] Hong S. et al.: Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review. Computers in Biology and Medicine 122, 2020, 103801
  Google Scholar

[https://doi.org/10.1016/j.compbiomed.2020.103801].
DOI: https://doi.org/10.1016/j.compbiomed.2020.103801   Google Scholar

[9] Hu J. et al.: Real-time Nondestructive Fish Behavior Detecting in Mixed Polyculture System Using Deep-learning and Low-cost Devices. Expert Systems With Applications 178, 2021, 115051 [https://doi.org/10.1016/j.eswa.2021.115051].
DOI: https://doi.org/10.1016/j.eswa.2021.115051   Google Scholar

[10] Iqbal M. A. et al.: Automatic Fish Species Classification Using Deep Convolutional Neural Networks. Wireless Personal Communications 116(2), 2019, 1043–1053 [https://doi.org/10.1007/s11277-019-06634-1].
DOI: https://doi.org/10.1007/s11277-019-06634-1   Google Scholar

[11] Isabelle D. A., Westerlund M.: A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation. Sustainability 14(4), 2022, 1979 [https://doi.org/10.3390/su14041979].
DOI: https://doi.org/10.3390/su14041979   Google Scholar

[12] Ismail N., Owais A. M.: Real-time Visual Inspection System for Grading Fruits Using Computer Vision and Deep Learning Techniques. Information Processing in Agriculture 9(1), 2022, 24–37 [https://doi.org/10.1016/j.inpa.2021.01.005].
DOI: https://doi.org/10.1016/j.inpa.2021.01.005   Google Scholar

[13] Jalal A. et al.: Fish Detection and Species Classification in Underwater Environments Using Deep Learning with Temporal Information. Ecological Informatics 57, 2020, 101088 [https://doi.org/10.1016/j.ecoinf.2020.101088].
DOI: https://doi.org/10.1016/j.ecoinf.2020.101088   Google Scholar

[14] Jing L. et al.: Video You Only Look Once: Overall Temporal Convolutions for Action Recognition. Journal of Visual Communication and Image Representation 52, 2018, 58–65 [https://doi.org/10.1016/j.jvcir.2018.01.016].
DOI: https://doi.org/10.1016/j.jvcir.2018.01.016   Google Scholar

[15] Khan A. N. et al.: Sectorial Study of Technological Progress and CO2 Emission: Insights From a Developing Economy. Technological Forecasting and Social Change 151, 2020, 119862 [https://doi.org/10.1016/j.techfore.2019.119862].
DOI: https://doi.org/10.1016/j.techfore.2019.119862   Google Scholar

[16] Khokher M. R. et al.: Early Lessons in Deploying Cameras and Artificial Intelligence Technology for Fisheries Catch Monitoring: Where Machine Learning Meets Commercial Fishing. Canadian Journal of Fisheries and Aquatic Sciences 79(2), 2022, 257–266 [https://doi.org/10.1139/cjfas-2020-0446].
DOI: https://doi.org/10.1139/cjfas-2020-0446   Google Scholar

[17] Klapp I. et al.: Ornamental Fish Counting by Non-imaging Optical System for Real-time Applications. Computers and Electronics in Agriculture 153, 2018, 126–133 [https://doi.org/10.1016/j.compag.2018.08.007].
DOI: https://doi.org/10.1016/j.compag.2018.08.007   Google Scholar

[18] Liu H., Lang B.: Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Applied Sciences 9(20), 2019, 4396 [https://doi.org/10.3390/app9204396].
DOI: https://doi.org/10.3390/app9204396   Google Scholar

[19] Mada Sanjaya W. S.: Deep Learning Citra Medis Berbasis Pemrograman Python. Bolabot, 2023.
  Google Scholar

[20] Redmon J. et al.: You Only Look Once: Unified, Real-Time Object Detection. arXiv.org, 8 June 2015, arxiv.org/abs/1506.02640.
DOI: https://doi.org/10.1109/CVPR.2016.91   Google Scholar

[21] Reynard D., Shirgaokar M.: Harnessing the Power of Machine Learning: Can Twitter Data Be Useful in Guiding Resource Allocation Decisions During a Natural Disaster? Transportation Research Part D: Transport and Environment 77, 2019, 449–463 [https://doi.org/10.1016/j.trd.2019.03.002].
DOI: https://doi.org/10.1016/j.trd.2019.03.002   Google Scholar

[22] Rico-Díaz Á. J. et al.: An Application of Fish Detection Based on Eye Search With Artificial Vision and Artificial Neural Networks. Water 12(11), 2020, 3013 [https://doi.org/10.3390/w12113013].
DOI: https://doi.org/10.3390/w12113013   Google Scholar

[23] Sanjaya W. S. et al.: The Design of Face Recognition and Tracking for Human-robot Interaction. 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering – ICITISEE). IEEE, 2017 [https://doi.org/10.1109/icitisee.2017.8285519].
DOI: https://doi.org/10.1109/ICITISEE.2017.8285519   Google Scholar

[24] Shafiee M. J. et al.: Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video. arXiv.org, 18 Sept. 2017, arxiv.org/abs/1709.05943.
DOI: https://doi.org/10.15353/vsnl.v3i1.171   Google Scholar

[25] Unlu E. et al.: Deep Learning-based Strategies for the Detection and Tracking of Drones Using Several Cameras. IPSJ Transactions on Computer Vision and Applications 11(1), 2019 [https://doi.org/10.1186/s41074-019-0059-x].
DOI: https://doi.org/10.1186/s41074-019-0059-x   Google Scholar

[26] Wang D. et al.: UAV Environmental Perception and Autonomous Obstacle Avoidance: A Deep Learning and Depth Camera Combined Solution. Computers and Electronics in Agriculture 175, 2020, 105523 [https://doi.org/10.1016/j.compag.2020.105523].
DOI: https://doi.org/10.1016/j.compag.2020.105523   Google Scholar

[27] Xiu L. et al.: Fast Accurate Fish Detection and Recognition of Underwater Images With Fast R-CNN. OCEANS 2015 – MTS/IEEE Washington. IEEE, 2015 [https://doi.org/10.23919/oceans.2015.7404464].
DOI: https://doi.org/10.23919/OCEANS.2015.7404464   Google Scholar

[28] Zhang L. et al.: Automatic Fish Counting Method Using Image Density Grading and Local Regression. Computers and Electronics in Agriculture 179, 2020, 105844 [https://doi.org/10.1016/j.compag.2020.105844].
DOI: https://doi.org/10.1016/j.compag.2020.105844   Google Scholar

[29] Zhao Zhong-Qiu et al.: Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems 30(11), 2019, 3212–3232 [https://doi.org/10.1109/tnnls.2018.2876865].
DOI: https://doi.org/10.1109/TNNLS.2018.2876865   Google Scholar


Opublikowane
2024-09-30

Cited By / Share

Multajam, R., Mohamad Ayob, A. F., Sanjaya, W. M., Sambas, A., Rusyn, V., & Samila, A. (2024). WYKRYWANIE I KLASYFIKACJA RYB W CZASIE RZECZYWISTYM W ŚRODOWISKU PODWODNYM PRZY UŻYCIU YOLOV5: BADANIE PORÓWNAWCZE ARCHITEKTUR GŁĘBOKIEGO UCZENIA. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 91–95. https://doi.org/10.35784/iapgos.6022

Autorzy

Rizki Multajam 

Universiti Malaysia Terengganu Malezja

Autorzy

Ahmad Faisal Mohamad Ayob 

Universiti Malaysia Terengganu Malezja

Autorzy

W.S. Mada Sanjaya 

Universitas Islam Negeri Sunan Gunung Djati Indonezja

Autorzy

Aceng Sambas 

Universiti Sultan Zainal Abidin Malezja

Autorzy

Volodymyr Rusyn 
rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraina
https://orcid.org/0000-0001-6219-1031

Autorzy

Andrii Samila 

Yuriy Fedkovych Chernivtsi National University Ukraina

Statystyki

Abstract views: 131
PDF downloads: 65


Licencja

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.