Feature-Based Template Matching for Joggled Fishplate Detection in Railroad Track with Drone Images

Joggled fishplate is an essential component that helps with emergency repairs to fractured sections of the rail. The monitoring of this component is essential in order to maintain steadiness of the train. Manual operations involve railroad personnel. These operations are subjective and time-consuming. Consequently, replacement of manual inspections with unmanned vehicles such as drones is well suited for fast and efficient maintenance operations. Therefore, in this work, we have proposed a critical analysis for development of a novel feature-based template matching technique for efficient and fast joggled fishplate detection in drone data. The appropriate feature is chosen for performing sliding window-based template matching using Euclidean distance. Non-maximum suppression has been used for reduction of false detections. The specificity and sensitivity values are calculated as 0.98 and 0.79 respectively.