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Zain Haq, Avip
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Comparative Analysis of Lightweight CNN Architectures for Railway Track Fault Detection Zain Haq, Avip
Journal FORTEI-JEERI Vol. 6 No. 2 (2025): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v6i2.35

Abstract

The application of artificial intelligence is widely used in various industrial sectors, including the transportation sector, one of which is the use of Image Processing to detect damage to railway lines. Railway conventional inspections involve visually examining and measuring railway infrastructure to identify potential problems. These inspections are an important aspect of ensuring the safety and efficiency of the railway network. In some places, railway track inspections still use conventional methods with electricity and vision. The use of artificial intelligence is expected to minimize errors, increase efficiency, reduce time and costs in train damage inspections. This research aims to find the best architecture and its development is expected to be used Accelerate the process of locating damage so that railroads can be repaired immediately. In this study, we evaluate the CNN model with the lightweight model to classify the image of the condition of the train track. Several types of lightweight models chosen are EfficientNetB0, EfficientNetB3, MobileNetV2, NasNetMobile. From the results of the evaluation carried out, it was found that EfficientNetB0 was 0.875, EfficientNetB3 was 0.958, MobileNetV2 was 0.917, and NasNetMobile was 0.8333.
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