Dealing with Low Quality Images in Railway Obstacle Detection System

Abstract

Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.

Development of a High-Precision Lidar System and Improvement of Key Steps for Railway Obstacle Detection Algorithm

“…To calculate the angle compensation for each pulse point, we utilized Equations ( 5) and (6). We also proposed a measurement of the error between the corrected and original coordinates using the Euclidean distance (R Euclidean ).…”

“…However, current optical systems face several challenges, including susceptibility to lighting conditions; reduced obstacle recognition in harsh environments such as rain, snow, and fog; and difficulty in accurately identifying the distance information of obstacles [5]. Staniša et al [6] proposed a machine learning-based strategy for obstacle detection in cases of pixel-level differences to overcome the low-pixel resolution of the camera system under adverse environmental conditions. Meanwhile, Anand et al [7] introduced a visual enhancement system that combines infrared cameras and other devices to improve its detection rate in adverse weather conditions.…”