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All Journal International Journal of Artificial Intelligence and Robotics (IJAIR) El-Mujtama: Jurnal Pengabdian Masyarakat INOVTEK Polbeng - Seri Informatika
Sari, Dewi Mutiara
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Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : INOVTEK Polbeng - Seri Informatika

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Underwater Single and Multiple Objects Detection Based on The Combination of YOLOv7-tiny and Visual Feature Enhancement Sari, Dewi Mutiara; Marta, Bayu Sandi; Dwito Armono, R. Haryo; Rizaldy Pratama, Alfan; Putra Pratama, Firmansyah
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91b9qn06

Abstract

Breakwater construction in Indonesia frequently employs tetrapods to dissipate wave energy. However, the placement process remains manual, relying on divers to guide crane operators. This approach not only poses safety risks but also limits visibility due to underwater turbidity. While prior research has focused on underwater image enhancement, the integration of tetrapod object detection remains unexplored. This study proposes a combined method of underwater image enhancement and tetrapod object detection to support land-based operator visualization. Auto-Level Filtering and Histogram Equalization techniques were applied to enhance image clarity, followed by object detection using the YOLOv7-tiny model. Tetrapod models at a 1:20 scale were used for training and testing. The proposed system achieved a mean average precision (mAP) of 0.95. Evaluation was conducted across 12 scenarios, involving four lighting levels and two water conditions: clear and 45.8% turbidity. The object detection confidence scores were 0.80 without enhancement, 0.85 with Histogram Equalization, and 0.84 with Auto-Level Filtering. Multiple object detection achieved an accuracy of 88.75%, outperforming previous approaches using YOLOv4-tiny. The results demonstrate the potential of integrating image enhancement and deep learning-based object detection for improving underwater operational safety and placement precision in breakwater construction.
Co-Authors Alfan Rizaldy Pratama Amin A, Muhammad Ardianto, Arya Andhika Arissabarno, Cahyo Azzahra, Salsabilla Azzahra, Soniya Jasmine Dadet Pramadihanto Dwito Armono, Haryo Dwito Armono, R. Haryo Fadhillah, Wiradika Nur Marta, Bayu Sandi Nadhori,  Isbat Izzudin Nugroho, Farrel Adhitama Putra Pratama, Firmansyah Rizaldy Pratama, Alfan Romadhoni,  Achmad Fatrian Tri Hadiah Muliawati, Tri Hadiah Wahyuda, Imam