Maskininlärning och bildtolkning för ökad tillförlitlighet i strömavtagarlarm

• Main Author: Clase, Christian
• Format: Others
• Language: Swedish
• Published: Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap 2018
• Subjects: maskininlärning; tensorflow; bildanalys; mönsterigenkänning; k-means; kluster; Engineering and Technology; Teknik och teknologier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27766

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. This coal rail lies against the contact wire and may become worn in such a way that damages are formed in the coal rail, which results in a risk of demolition of the contact wire which causes major interference and high costs. Today, approximately 10 demolitions of contact wire occur annually due to missed detection. Today's system is called KIKA2, developed during the year 2011 and incorporates a 12 MP digital camera, a target radar and detection of a damaged pantographs is done using various famous imaging techniques. The shortcomings of today's system are that the proportion of false alarms is high and on these occasions, a person must manually review the pictures.   The purpose of this degree project is to propose improvements and explore the possibilities of working with TensorFlow machine learning.  I have used different image processing techniques on the KIKA2 images for optimizing the images for TensorFlow machine learning. I realized after some TensorFlow classification tests on the raw images that preprocessing the images is necessary to obtain realistic values for the classification part. My plan was to clean the pictures from noise, in other words crop the coal rail and improve the contrast to make the damages in the coal rail more visible.  I have used Fourier analyze and correlation techniques to crop the coal rail and the k-means classification algorithm to improve the contrast of the images. The Googles TensorFlow is an open source framework and to use pre-processed RGB images from today's system KIKA2 will give reasonable classification values. I have brought some IR images with an external heating camera (FLIR-E60) of the pantograph. I can see that the thermal camera provides very nice contours on the pantograph, which is very good for machine learning.  My recommendation is that for further studies is to further evaluate the IR technique and use IR-images taken from different angles, distances and with different backgrounds. The segmentation of the images can be done with either Hu´s moment or Fourier analysis with correlation and refined with for example classification techniques. IR images could be used to complement today's systems, or machine learning together with today's RGB images. A robust and proven pre-treatment technique is very important for obtaining good results in machine learning and requires further studies and real life tests to handle different types of pantographs, different light conditions and other differences in the images.