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All Journal Sinergi The Indonesian Journal of Computer Science
Lahimade, Vicky Nolant Setyanto
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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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.
An effective and efficient vehicle detection using ER-EMA-YOLOv10n Kutika, Imanuel; Lahimade, Vicky Nolant Setyanto; Todingan, Tomi Heri Julius; Prasetya, Hebron; Sentinuwo, Steven Ray; Putro, Muhamad Dwisnanto
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Vehicle detection plays a key role in automating traffic analysis, a field that continues to advance rapidly. Vision-based systems identify vehicle types and sizes, but achieving high accuracy and efficiency remains a challenge. Reliable real-world deployment requires optimized models that balance performance and computational cost. YOLOv10n, the most efficient version of the YOLO family, offers a solid foundation for lightweight feature extraction. To improve its detection performance, this study proposes an enhanced version of YOLOv10n by incorporating a scale-aware attention mechanism. We proposed the Expanded Refinement Efficient Multi-Scale Attention (ER-EMA) module, which enhances feature encoding by capturing vehicle characteristics across multiple receptive fields. ER-EMA consists of two core components: the Expanded Converted Inverted Block (ECIB) and the Convolutional Refinement Block (CRB). These components use diverse convolutional kernels to extract and refine multi-frequency spatial features. Integrating ER-EMA into the YOLOv10n framework produces a more compact and accurate detection model. Experimental results show that the proposed model increases mAP@50 by 1%, while reducing the number of parameters by 0.1M and computation by 0.1 GFLOPS on the Vehicle-COCO dataset. On the UA-DETRAC benchmark, it achieves a 4% improvement in mAP@50:95, with a reduction of 0.2M in parameters and 0.4 GFLOPS in computational efficiency—outperforming the original YOLOv10n and prior methods in both performance and computational efficiency.
Co-Authors Alwin M. Sambul, Alwin M. Kutika, Imanuel Oktavian A. Lantang Prasetya, Hebron Putro, Muhamad Dwisnanto Steven Ray Sentinuwo Todingan, Tomi Heri Julianus Todingan, Tomi Heri Julius