The article considers one of the approaches to solving the problem of automated detection of defects of the upper structure of the railway track on the basis of image processing methods. The whole problem is broken into a series of sequential subtasks: image preprocessing, detection of rail defects, rail fasteners search, identification of fasteners that are suspected to defect. For preprocessing, an approach based on the use of matrix filters for the convolution operation was used. The solution of the problem of finding rail defects was performed on the basis of the algorithm for finding areas other than a given color within a given rectangular area. Rail fasteners were identified on the basis of the pattern matching algorithm adapted to the characteristics of the subject area. In view of the variety of defects, it was decided to divide the images into two classes: with and without defects, followed by the transmission of images suspicious of the defect for manual processing. The images are divided into four groups:
- mistakes; in such images of bonds or not at all, or they are not completely caught in the perspective due to inaccuracies of the algorithm for finding bonds;
- real defects; on such images of fastening or are absent in view of malfunction, or there is other breakage;
- perfect staples; here the staples images are free of defects and are fully visible;
- other; in such images there are white spots, debris, bonds can be covered with stones, covered with other objects and more.
All non-ideal images are considered suspicious of the defect. A convolutional neural network was used to separate them from the ideal ones. The issues of training such a network are discussed
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