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All Journal JOIV : International Journal on Informatics Visualization Techne : Jurnal Ilmiah Elektroteknika The Indonesian Journal of Computer Science
Irwansyah, Arif
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : JOIV : International Journal on Informatics Visualization

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Real-Time Embedded Vision System for Road Damage Detection Utilizing Deep Learning Putri, Ambarwati Rizkia; Irwansyah, Arif; Arifin, Firman; Purwantini, Elly; Wijaya, Candra Kusuma
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3661

Abstract

Accidents resulting from road damage are becoming a serious concern, emphasizing the need for efficient monitoring systems and timely government intervention. This research highlights the potential of advanced AI-driven solutions in road safety management, providing a practical approach to efficiently monitoring and maintaining road conditions. It presents a real-time embedded vision system for automatic road damage detection using deep learning techniques. The system is designed to classify six types of road damage and has been implemented on two platforms: Jetson Nano and a personal computer or laptop. A comparative analysis was conducted to evaluate accuracy, computational performance, and power efficiency. The study employs YOLO (v5, v7, v8) and EfficientDet algorithms for detecting road damage. Experimental results indicate that EfficientDet achieves the highest accuracy at 88%, while YOLO attains 63%. In terms of computational performance, YOLOv8 delivers the highest frame rate, reaching 25 FPS on the Jetson Nano. Power efficiency analysis reveals that YOLOv8 on the Jetson Nano is six times more energy-efficient compared to its implementation on a laptop. Likewise, EfficientDet on Jetson Nano demonstrates three times better energy efficiency than on a laptop. These findings underscore the feasibility of deploying AI-powered embedded vision systems for detecting road damage. The use of deep learning models on energy-efficient platforms, such as Jetson Nano, enhances real-time performance while minimizing power consumption. Future research should focus on optimizing these models to enhance performance on edge devices while further assessing their practical applications in real-world environments.
Comparative Study of CNN Architectures for Real-Time Audio-Based Car Accident Detection on Edge Devices Ilahi, Ahmada Haiz Zakiyil; Irwansyah, Arif; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2985

Abstract

Traffic accidents often result in fatalities for both drivers and bystanders. Traditionally, accident information relies heavily on community reports, which can delay the provision of victim assistance. To address this issue, a system capable of detecting accidents responsively in various weather conditions and traffic densities is necessary. One approach involved using audio analysis techniques to evaluate collision sounds. Thus, this study proposed an audio classification system for detecting car accidents using Convolutional Neural Networks (CNNs). The system’s performance was evaluated on personal computers and edge devices, such as the Raspberry Pi 4 and NVIDIA Jetson Nano, to compare inference times and power consumption. To enhance the dataset, segmentation and augmentation techniques were applied before converting the audio data into a 2D Mel-spectrogram. The dataset was then trained and assessed with four CNN architectures: custom sequential, custom with shared input layer, transfer learning EfficientNetB0, and transfer learning MobileNetV2. Both original and Lite models were deployed on experimental devices. Results showed that the custom CNN model had faster inference times across devices in both original and lite forms, though it had a 4% increase in the false positive rate. The Lite MobileNetV2 model recorded the fastest inference time on edge devices at 86 ms. Jetson Nano exhibited faster inference times compared to Raspberry Pi 4. However, Raspberry Pi 4 showed a minor increase in power consumption of 0.6 watts during inference. In future work, this system can be tested in real-time environments using embedded systems to evaluate its robustness against noise and varying environmental conditions.
Co-Authors Ahmada Haiz Zakiyil Ilahi Akbar, Reesa Budi Nur Iman Elly Purwanti Firman Arifin, Firman Hary Oktavianto Ilahi, Ahmada Haiz Zakiyil Naufal Mukhfi Robbani Putra, Arie Zuliyan Putri, Ambarwati Rizkia Wijaya, Candra Kusuma Zazuli, Moh Ismarintan