Sökning: "Rail Image Classification"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Rail Image Classification.

1. 1. Detecting Defective Rail Joints on the Swiss Railways with Inception ResNet V2 : Simplifying Predictive Maintenance of Railway Infrastructure

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Anton Lu; [2022]
   Nyckelord :Rail Defect Detection; Rail Image Classification; Rail Joints; Object Detection; Image Classification; Deep Learning; Järnvägsfeldetektering; Järnvägsbildigenkänning; Järnvägsskarvar; Objectigenkänning; Bildklassificering; Djupinlärning;

   Sammanfattning : Manual investigation of railway infrastructure is a labor-intensive and time-consuming task, and automating it has become a high priority for railway operators to reduce unexpected infrastructure expenditure. In this thesis, we propose a new image classification approach for classifying defect and non-defective rail joints in image data, based on previous fault detection algorithms using object detection. LÄS MER

3. 2. A Machine Learning-Based Approach for Fault Detection of Railway Track and its Components

   Master-uppsats, Luleå tekniska universitet/Drift, underhåll och akustik

   Författare :Johnny Asber; [2020]
   Nyckelord :Condition Based Maintenance in Railway; Machine Learning; Deep Learning; ResNet-50;

   Sammanfattning : The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. LÄS MER

4. 3. Maskininlärning och bildtolkning för ökad tillförlitlighet i strömavtagarlarm

   Master-uppsats, Högskolan i Gävle/Avdelningen för elektronik, matematik och naturvetenskap

   Författare :Christian Clase; [2018]
   Nyckelord :maskininlärning; tensorflow; bildanalys; mönsterigenkänning; k-means; kluster;

   Sammanfattning : This master´s degree project is carried out by Trafikverket and concerns machine learning and image detection of defective pantographs on trains.   Today, Trafikverket has a system for detecting damages of the coal rail located on the pantograph. LÄS MER

5. 4. MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

   Master-uppsats, Datateknik

   Författare :Mihira Manne; [2009]
   Nyckelord :Artificial intelligence; Non-destructive testing; Rail inspection; Rail transportation; Unsupervised learning; Data fusion;

   Sammanfattning : The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection. LÄS MER

7. 5. Real time video segmentation for recognising paint marks on bad wooden railway sleepers

   Master-uppsats, Högskolan Dalarna/Datateknik

   Författare :Asif ur Rahman Shaik; [2008]
   Nyckelord :Condition Monitoring; Intelligent Vehicle; Videos; Color Segmentation; Spots objects ; Regions;

   Sammanfattning : Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. LÄS MER