Classification of Road Damage from Digital Image Using Backpropagation Neural Network - Sutikno Sutikno; Helmie Arif Wibawa; Prima Yusuf Budiarto (IAES International Journal of Artificial Intelligence (IJ-AI), 2017)
One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.