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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Rekayasa Material, Manufaktur & Energi Adimas : Jurnal Pengabdian Kepada Masyarakat 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
Adiratna Ciptaningrum
Politeknik Negeri Madiun
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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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 Alfiyani, Risma Apriyanto, R. Akbar Nur Arum, Widya Sekar Bi Asngali Dimas Nur Prakoso Faikhsan, Edo Zulmi Fita Sari, Fita Ida Ayu Putu Sri Widnyani Imam Junaedi Jamil, Zhulfi Fadhillah Mudmainah, Putri Hana Widyaning Nadia, Hana Jasmine Pangestu, Djadug Suryo Pratama, R. Gaguk Putri, Larissa Kartika R. Oktav Yama Hendra Rahayu Mekar Bisono, Rahayu Mekar Saskya, Lorensa Firda Sembiring, Rinawati Syah Putra, Muhammad Dio Wahyu Pribadi Wicaksono, Darma Arif Wicaksono, Ramadhana Eka Wida Yuliar Rezika, Wida Yuliar Yudha, R. Gaguk Pratama Yudhiatama, Galang Dafa