Railway transportation plays a vital role in modern infrastructure by providing efficient and reliable movement of passengers and goods. However, railway track faults such as cracks, misalignments, and structural damage can lead to severe accidents, service disruptions, and significant economic losses if not detected at an early stage. Traditional railway track inspection methods mainly rely on manual monitoring and scheduled maintenance procedures, which are time-consuming, labour-intensive, and prone to human error. With the growing expansion of railway networks and increasing train speeds, there is a strong need for intelligent and automated systems capable of detecting track faults accurately and efficiently. This study proposes an automated railway track fault detection framework based on image processing and fuzzy logic techniques. The proposed system utilizes a vision-based approach in which images of railway tracks are captured using an embedded camera system and processed to identify potential defects. Image preprocessing techniques such as grayscale conversion, noise filtering, and segmentation are applied to enhance the quality of captured images and isolate important track features. Edge detection and thresholding methods are used to identify cracks or abnormalities present on the railway track surface. This intelligent classification approach helps reduce false detections while improving decision-making accuracy. Experimental results demonstrate that the proposed system can effectively identify track faults and provide reliable early warnings for railway maintenance teams. By combining image processing techniques with fuzzy logic-based decision support, the proposed framework enhances railway safety by enabling automated, real-time track inspection. The system can significantly reduce manual inspection effort, improve fault detection accuracy, and support proactive maintenance strategies for modern railway infrastructure. \\
Sai et al. (Thu,) studied this question.