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All Journal Jurnal Telematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Alwin M. Sambul
Universitas Sam Ratulangi
Computer Science & IT Education Electrical & Electronics Engineering

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Studi Perbandingan Model Lightweight YOLOv12 untuk Deteksi Objek Bawah Air Secara Real-Time Hebron Prasetya; Revin R. Balo; Tasya Tumbal; Alwin M. Sambul; Muhamad Dwisnanto Putro
Jurnal Telematika Vol. 20 No. 2 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i2.799

Abstract

Deep learning methods in computer vision play a crucial role in object localization using camera-based sensors, with Convolutional Neural Networks serving as the dominant approach for object detection. However, many existing models incur high computational costs due to deep architectures and complex operations, limiting their use for real-time deployment on low-cost, resource-constrained devices. The YOLOv12 architecture offers lightweight variants to improve computational efficiency. This study evaluates the trade-off between efficiency and detection performance by comparing model variants using the number of parameters, floating-point operations, and inference speed, while detection accuracy is measured using mean average precision. The results assess the suitability of lightweight models for real-time deployment in resource-constrained environments such as underwater monitoring and conservation. Experimental results on the Real-World Underwater Object Detection dataset demonstrate that YOLOv12-nano achieves 5.7% lower accuracy compared to YOLOv12-medium but requires only 2.57 million parameters and 6.5 GFLOPs, significantly less than YOLOv12-medium with 20.1 million parameters and 67.8 GFLOPs. Moreover, YOLOv12-small requires 9.26 million parameters and 21.5 GFLOPs, positioning it between nano and medium in terms of complexity while still maintaining competitive accuracy. In the inference process, YOLOv12-nano achieves 16.48 FPS on a 12th Gen Intel(R) Core (TM) i5-12450HX CPU. In comparison, YOLOv12-small runs at 6.28 FPS, while YOLOv12-medium runs at 2.36 FPS. These results indicate that YOLOv12-nano is the most suitable variant for real-time deployment on CPU-based platforms.
DOLPHIN DETECTION USING AN ENHANCED LIGHTWEIGHT YOLO ARCHITECTURE Febriyanti Ludja; Robby Moody Lintong; Florensce Sumarauw; Alwin M. Sambul; Steven R. Sentinuwo; Muhamad Dwisnanto Putro
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.9169

Abstract

Dolphin detection plays an important role in marine ecosystem monitoring, species conservation, and behavioral analysis. However, visual identification in underwater environments faces challenges such as light refraction, water turbidity, and dynamic sea conditions. This study proposes a deep learning-based dolphin detection approach by modifying the YOLOv8 architecture to produce a lightweight yet accurate model. The modifications include reducing the number of channels in the backbone and neck, as well as simplifying the SPPF block, thereby reducing the model parameters from 3.01 million to 1.83 million and the computational complexity from 8.2 GFLOPs to 7.2 GFLOPs. A specialized dolphin dataset consisting of 5,493 labeled images, collected from underwater and surface conditions, was developed to train and evaluate the model. Experimental results show that the proposed model achieves 67.1% mAP@50 and 45.8% mAP@50–95, outperforming YOLOv8-Nano and other lightweight YOLO variants. Additionally, the model demonstrates better runtime efficiency, with a latency of 49.2 ms and 20.38 FPS, making it suitable for real-time implementation on resource-constrained devices. Overall, this research presents a more efficient and accurate dolphin detection solution, while also providing a specialized dataset that can support further research in the field of computer vision-based marine conservation.
Co-Authors Febriyanti Ludja Florensce Sumarauw Hebron Prasetya Putro, Muhamad Dwisnanto Revin R. Balo Robby Moody Lintong Steven R. Sentinuwo Tasya Tumbal