Railway Track Sleeper Detection in Low Altitude UAV Imagery Using Deep Convolutional Neural Network

Abstract

Railway track sleepers are the most critical component that serves as the backbone for other supplementary components. Frequent monitoring of sleepers ensures better railway track health conditions that ensure the safety of goods and passengers. Recently railway explored various monitoring possibilities based on UAV for the robust, cost effective, and efficient inspection of railway track components. Deep learning based object detectors show promising results on large and small datasets due to increased computation resources. YOLO versions of object detection model are most superior on low altitude aerial images. This paper explores the possibility of railway track sleeper detection using a custom object detection model based on the YOLO v4 algorithm in low altitude UAV images.