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All Journal Jurnal Teknologi JMPM (Jurnal Material dan Proses Manufaktur) Jurnal Rekayasa Hijau TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Applied Technology and Computing Science Journal Jurnal Teknik Elektro dan Komputasi (ELKOM) International Journal of Science, Engineering and Information Technology Journal Geuthee of Engineering and Energy TANJAK : Jurnal Pengabdian Kepada Masyarakat
Apriyanto, R. Akbar Nur
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Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Applied Technology and Computing Science Journal

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A Dual-Stage Hybrid Vision Framework Using YOLOv8n-Canny Edge Detection for Real-Time Railway Trespassing and Intrusion Monitoring Ciptaningrum, Adiratna; Echsony, Mohammad Erik; Apriyanto, R. Akbar Nur
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8579

Abstract

Intrusion and trespasser detection on railway tracks is a crucial safety measure to prevent accidents and maintain operational reliability. This study proposes a hybrid vision-based approach that integrates YOLOv8n, a lightweight real-time object detection model, with the Canny edge detection algorithm to identify and classify unauthorized objects and individuals on railway tracks. In this context, intrusions refer to inanimate objects such as rocks, fallen trees, or construction materials obstructing the tracks, whereas trespassers refer to humans or other living beings engaging in unauthorized activities near or on the railway line. YOLOv8n is employed as a single-stage detector to localize and classify objects, while Canny edge detection is applied to enhance object contours and improve shape-based differentiation between intrusion and trespasser categories. Experimental results show an average accuracy of 52.37%, indicating moderate detection performance. Although the accuracy remains limited, the findings demonstrate the potential of combining deep learning and traditional image processing techniques to develop an automated monitoring system that supports railway safety and surveillance applications. Further optimization of the dataset, model tuning, and feature enhancement are recommended to improve detection performance.
Co-Authors Adiratna Ciptaningrum Ardhyanti, Novi BASUKI, GAMAR Dimas Nur Prakoso Dyah, Ainun Purwining Era Purwanto Faikhsan, Edo Zulmi Fakhruddin, Hanif Hasyier Gigih Prabowo Hakiki, Zahrani Aniela Hary Oktavianto Hibbanurrohim, Muhammad Ibram Imam Junaedi Nurdiansyah, Rahardian Titus Pratama, R. Gaguk Putri, Larissa Kartika R. Oktav Yama Hendra Rahayu Mekar Bisono, Rahayu Mekar Salim, Alfi Tranggono Agus Sembiring, Rinawati Syah Putra, Muhammad Dio Wicaksono, Darma Arif Yahya, Hafid Mustofa Yudha, R. Gaguk Pratama Yuwono, Indarto