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All Journal International Journal of Artificial Intelligence and Robotics (IJAIR) INOVTEK Polbeng - Seri Informatika
Sari, Dewi Mutiara
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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The Analysis of Underwater Imagery System for Armor Unit Monitoring Application Sari, Dewi Mutiara; Marta, Bayu Sandi; Amin A, Muhammad; Dwito Armono, Haryo
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.168 KB) | DOI: 10.25139/ijair.v5i1.5918

Abstract

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.
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 Dadet Pramadihanto Dwito Armono, Haryo Dwito Armono, R. Haryo Marta, Bayu Sandi Putra Pratama, Firmansyah Rizaldy Pratama, Alfan