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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Gilang Septia Firmansyah
Universitas Muhammadiyah Lamongan
Computer Science & IT

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Comparative deep learning architectures for railway train detection using accelerometer Heri Ardiansyah; Denny Irawan; Dhio Ramadhon Saripudin; Gilang Septia Firmansyah; Rohmatin Agustina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27752

Abstract

Railway level crossing accidents remain a critical safety concern worldwide, necessitating reliable train detection systems particularly at unguarded crossings. This study presents comparative evaluations from four deep learning (DL) architectures convolutional neural network (2D-CNN), long short-term memory (LSTM), MobileNetV2, and hybrid CNN-LSTM of railway train detection using triaxial accelerometer sensor data. Vibration signals acquired from piezoelectric sensors mounted on railway infrastructure were transformed into spectrogram representations and classified into four proximity categories: idle, far-distance (>400 m), near distance (200-400 m), and train passing (
Co-Authors Agustina, Rohmatin Denny Irawan Dhio Ramadhon Saripudin Heri Ardiansyah