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All Journal PROtek : Jurnal Ilmiah Teknik Elektro JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
Putro, Muhamad Dwisnanto
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Speedy Vision-based Human Detection Using Lightweight Deep Learning Network Aktama, Gede Erik; Manoppo, Franky; Simbolon, Rosdiana; Laloan, Adityo Clinton; Sumendap, Andreas; Putro, Muhamad Dwisnanto
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i2.7030

Abstract

Person detection plays a role as the initial system of video surveillance analysis with various implementations, such as activity analysis, person re-id, behavior analysis, and tracking analysis. The demand for efficient models drives a deep learning architecture with a superficial structure that can operate in real-time. You look only once (YOLO) object detection has been presented as an accurate detector that can operate in real-time. The speed limitation, huge computation cost, and abundant parameters still leave vital issues to improve the efficiency of this architecture. Lightweight human detection is proposed by utilizing the YOLOv5n framework. Modifying layer depth promotes a detection system that can operate fast and without stuttering. As a result, the proposed detector has satisfactory performance and is competitive with existing models. It achieves a mAP of 45.2%, closely competing with other person detectors. Additionally, it can run fast without stumbling at 26 frames per second. The detector's speed offers the advantage of this work that it can be feasibly implemented on a cpu device without a graphics accelerator.
Sebuah Deteksi Sampah Tenggelam menggunakan Modul Eksitasi Reseptif Ganda Todingan, Tomi Heri Julianus; Kutika, Imanuel; Lahimade, Vicky Nolant Setyanto; Sambul, Alwin M.; Lantang, Oktavian A.; Putro, Muhamad Dwisnanto
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4635

Abstract

Sunken litter poses a severe ecological challenge, threatening marine life and global ecosystems. Plastic litter is particularly concerning as it could disrupt the food chain, impacting the biodiversity and ecosystem. Over time, without intervention, this issue poses a severe threat to global food security, economic stability in coastal communities, and overall environmental balance. Addressing this problem requires effective monitoring systems for detection. This study enhances the YOLOv10 architecture with a novel Dual Receptive Excitation (DRE) module to improve sunken litter detection. The DRE module uses a dynamic dual-kernel approach to balance spatial and channel-wise processing in Convolutional Neural Networks, adaptively adjusting the receptive field, and capturing critical patterns across scales. Evaluations on the challenging Trash-ICRA19 dataset, sourced from J-EDI, demonstrate the model's robustness under diverse underwater conditions. The proposed system achieves a mean average precision (mAP) of 47.4% and processes 19.60 frames per second, outperforming other studies.
DOLPHIN DETECTION USING AN ENHANCED LIGHTWEIGHT YOLO ARCHITECTURE Ludja, Febriyanti; Lintong, Robby Moody; Sumarauw, Florensce; Sambul, Alwin M.; Sentinuwo, Steven R.; Putro, Muhamad Dwisnanto
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.
Streamlining Deep Learning Network for Real-time Sea Turtle Detection Putro, Muhamad Dwisnanto; Mose, Yuliana; Andaria, Alex Copernikus; Litouw, Jane; Poekoel, Vecky Canisius; Najoan, Xaverius
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.35236

Abstract

Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed.
Co-Authors Alwin M. Sambul, Alwin M. Andaria, Alex Copernikus Dedi Sorongan, Yuliana Mose, Reonaldy A Berikang Franky Manoppo, Franky Gede Erik Aktama Jane Litouw, Jane Kutika, Imanuel Lahimade, Vicky Nolant Setyanto Laloan, Adityo Clinton Lintong, Robby Moody Ludja, Febriyanti Oktavian A. Lantang Poekoel, Vecky Canisius Simbolon, Rosdiana Steven Ray Sentinuwo Sumarauw, Florensce Sumendap, Andreas Todingan, Tomi Heri Julianus Xaverius Najoan