Squat flaws, when left unsupervised, can pose a threat to the safety of railway traffic. Unfortunately, as surface flaws, they cannot be detected by traditional ultrasonic methods. The application of eddy current to their detection is extremely difficult in the conditions occurring on the railway track. It seems that vision-based techniques can be a good alternative solution. The paper presents an algorithm allowing for the detection of these flaws. It uses wavelet transform to extract the rail from the background of the image. A Gabor filter bank along with a support vector machine (SVM) as a classifier were used in the squat detection process. The optimal number of features used to discriminate between the squat and the area without squat was selected with the help of the sequential feature selection method. The overall classification rate for the system was 93%.
The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.
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