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A Road Crack Detection Algorithm Based on SIFT Feature and BP Neural Network

Pavement cracks are an important hidden danger for driving safety. Rapid and accurate detection of road crack types and effective treatment can greatly reduce the incidence of traffic accidents, which has important practical significance. Based on the advantages of scale invariant feature transform (SIFT) and back-propagation neural network (BP) algorithm, this paper proposes a method of road crack detection and recognition for four types of cracks, such as cracks, longitudinal cracks, transverse cracks and massive cracks.Firstly, the crack image is denoised and enhanced; Then scale invariant feature transform method is used to extract the feature point information of the route crack image; Finally, Back propagation neural network is used for training and recognition. The results show that this method can quickly and accurately identify the crack type, in which the accuracy of training set is 98.79%, and the accuracy of recognition set is 86.47%. The results show that the combination of scale invariant feature transform and back propagation neural network algorithm can effectively improve the efficiency of pavement crack type detection.