Dealing with Low Quality Images in Railway Obstacle Detection System
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image and Video Quality Assessment · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, Serbia
Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Faculty of Mechanical Engineering, Department of Mechanical Design, Development, and Engineering, University of Niš, 18000 Niš, Serbia
Funding
This research received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme
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