LOBSTER AGE DETECTION USING DIGITAL VIDEO-BASED YOLO V8 ALGORITHM

Bayu Nusman; Aviv Yuniar Rahman; Rangga Pahlevi Putera

doi:10.52436/1.jutif.2024.5.4.2144

Bayu Nusman Department of Informatics, Faculty of Engineering, Universitas Widyagama Malang, Indonesia
Aviv Yuniar Rahman School of Graduate Studies, Doctor of Philoshopy in Information & Communication Technology, Asia e University, Selangor, Malaysia
Rangga Pahlevi Putera Department of Electrical Engineering Interest in Communication and Information Systems, Faculty of Engineering, Universitas Brawijaya, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2144

Keywords: Age Detection, Lobster, mAP50, YOLOv8

Abstract

Lobster is an aquatic animal that has high economic value in the fishing industry. Demand for lobster in both domestic and export markets continues to increase thanks to its delicious meat and a variety of desirable dishes. Indonesia, especially Java Island, contributes significantly to the national lobster production. However, the current manual determination of lobster age has limitations such as complexity, time required, and subjectivity in assessment.To overcome this problem, this research proposes the detection of lobster age using the YOLO (You Only Look Once) method, specifically the YOLOv8 version. This algorithm is known to be able to perform image and video recognition quickly and produce high accuracy. YOLOv8 can be run using a GPU, enabling parallel operations that significantly increase the speed of object detection compared to using a CPU alone. The data processing in this study involves several stages, starting from pre-processing in the form of image extraction and bounding from lobster videos. Next, the YOLOv8 algorithm was used to train the model with customized grid and bounding box parameters. The trained model is then validated and tested using lobster image and video data. The results of the test show that the developed YOLOv8 model has a precision of 0.997, recall of 0.998, mAP50 of 0.995, and mAP50-95 of 0.971. This shows that the model is able to detect and determine the age of the lobster with very high accuracy, providing a more efficient and objective solution than the manual method.

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