Open Access Open Access  Restricted Access Subscription or Fee Access

Deep Learning Methods for Classification of Road Defects

Nguyen T. Huong, Nguyen T. Long


Many methods are proposed in the classification of pavement defects through the extraction of features from data and the use of machine learning algorithms to solve the problem. But there are still limitations such as training time, accuracy and the sensitivity of the system to environmental conditions. This research proposes the method of optimizing the automatic classification system of pavement defects based on Convolutional Neural Network, a deep learning method commonly used and highly effective in artificial intelligence and digital image processing. This system is guaranteed to operate stably in limited light conditions, shading and complex-shaped defects. Our experiment is performed with 3 data sets (INESC TEC - Portugal, Irkutsk - Russia, Thai Nguyen - Viet Nam). The data obtained from VGG-16 method is compared with data obtained from the Random Forest algorithm and Support Vector Machine method. The experimental data show that the proposed method allows the Random Forest algorithm to work faster, more stable and gives more accurate results. The result after classification is 97.07 %, 97.62 %, 98.50 %) respectively.


computer vision, artificial intelligence, classification of road defects, features extraction, convolutional neural network, deep learning, ROC curves.

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.