Defect detection on videos using neural network
Roman Sizyakin, Nikolay Gapon, Igor Shraifel, Svetlana Tokareva, Dmitry A. Bezuglov · MATEC Web of Conferences · 2017
In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.