Pavement inspection is one of the most important steps in implementation of pavement management system and extend efforts have been done to increase the efficiency of this system by using the new technologies. In recent years, transportation agencies focus on creating automatic and more efficient systems for pavement inspection and a large number of researches have been done for this aim. According to progress of computer science, data mining and machine learning as computer-based methods are used more in various areas (such as engineering, medical and economy) and significant results have been achieved. In pavement management area, several researches have been performed with aim of the applying the machine learning especially in pavement distresses evaluation. In this paper, the theoretical concepts have been explained and several models have been created based on deep convolutional networks using transfer learning to detect and classify pavement CRACKS as the most prevalent pavement distress, and the performance of these models has been evaluated considering learning and test speed, and accuracy as the most important performance parameters. The results of this research indicate that the speed of models almost depends on the pre-trained models characteristics that applied in transfer learning process. Also the accuracy of models based on various metrics (Sensitivity, F-score, etc. ) is in range of 0. 94 to 0. 99 and indicates that deep learning method can be used to create expert systems for detection, classification, and quantification of pavement distresses such as cracking.