Catfish Fry Detection and Counting Using YOLO Algorithm
Abstract
The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 % Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.
Downloads
References
Q. Aini, N. Lutfiani, H. Kusumah, and M. S. Zahran, “Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo,” CESS (Journal Comput. Eng. Syst. Sci., vol. 6, no. 2, p. 192, 2021, doi: 10.24114/cess.v6i2.25840.
Jupiyandi Saniputra, F. R. Pratama, and Yoga Dharmawan, “Pengembangan Deteksi Citra Mobil Untuk Mengetahui Jumlah Tempat Parkir Menggunakan Cuda Dan Modified Yolo Development of Car Image Detection To Find Out the Number of Parking Space Using Cuda and Modified Yolo,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 4, pp. 413–419, 2019, doi: 10.25126/jtiik.201961275.
P. Hidayatullah, Buku Sakti Deep Learning: Computer Vision Menggunakan YOLO Untuk Pemula. Cimahi: Stunning Vision Al Academy, 2021.
H. Gomes, N. Redinha, N. Lavado, and M. Mendes, “Counting People and Bicycles in Real Time Using YOLO on Jetson Nano,” Energies, vol. 15, no. 23, Dec. 2022, doi: 10.3390/en15238816.
R. A. Utama, “Implementation Counting and Yolo Object Detection Methods for Identification Degree of Road Saturation,” J. Syst. Eng. Inf. Technol., vol. 1, no. 1, pp. 33–39, 2022, doi: 10.29207/joseit.v1i1.1965.
G. Wang, A. Feng, C. Gu, and X. Liu, “YOLO-DFD: A Lightweight Method for Dog Feces Detection Based on Improved YOLOv4,” J. Sensors, vol. 2023, 2023, doi: 10.1155/2023/5602595.
J. Cen et al., “An Improved Ship Classification Method Based on YOLOv7 Model with Attention Mechanism,” Wirel. Commun. Mob. Comput., vol. 2023, no. 2, 2023, doi: 10.1155/2023/7196323.
X. Lin, S. Wang, Z. Sun, and M. Zhang, “YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach,” J. Adv. Transp., vol. 2021, 2021, doi: 10.1155/2021/7534739.
B. Valarmathi et al., “Human Detection and Action Recognition for Search and Rescue in Disasters Using YOLOv3 Algorithm,” J. Electr. Comput. Eng., vol. 2023, 2023, doi: 10.1155/2023/5419384.
M. Masril, “Menghitung Hpp Bibit Lele Dan Pembuatan Laporan Keuangan Pada Usaha Pembibitan Lele Di Kampung Lele Desa Hangtuah Kecamatan Perhentian Raja Kabupater Kampar,” J. ABDIMAS STMIK Dharmapala, vol. 1, no. 1, pp. 13–17, 2021, doi: 10.47927/jasd.v1i1.83.
A. Glenn-jocher, Laughing-q, “Object Detection,” 2023. https://docs.ultralytics.com/tasks/detect/ (accessed Nov. 16, 2023).
B. A. Septyanto, “Implementasi Face Recognition Berbasis Deep Neural Network Sebagai Sistem Kendali Pada Quadcopter Implementation Of Face Recognition Based On Deep Neural Network As Control System On Quadcopter,” vol. 8, no. 6, pp. 3036–3050, 2022.
A. R. Wasril, M. S. Ghozali, and M. B. Mustafa, “Pembuatan Pendeteksi Obyek Dengan Metode You Only Look Once (Yolo) Untuk Automated Teller Machine (Atm),” Maj. Ilm. UNIKOM, vol. 17, no. 1, pp. 69–76, 2019, doi: 10.34010/miu.v17i1.2240.
R. A. Hamzah, C. Setianingsih, R. A. Nugrahaeni, S. R. Hanafia, and F. Fuadi, “Parking Violation Detection on The Roadside of Toll Roads with Intelligent Transportation System Using Faster R-CNN Algorithm,” Proc. - Int. Conf. Informatics Comput. Sci., vol. 2022-September, no. 3, pp. 169–174, 2022, doi: 10.1109/ICICoS56336.2022.9930590.
Jai-wei, “YoloSide - YOLOv8 GUI By PySide6.” github.com, p. 9, 2023. [Online]. Available: https://github.com/Jai-wei/YOLOv8-PySide6-GUI#readme
Copyright (c) 2023 Takyudin Takyudin, Iskandar Fitri, Yuhandri Yuhandri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
