PURPOSES: In this study, algorithms were proposed for determining the crack condition of an asphalt pavement image using deep learning methods. METHODS: For the configuration of a deep learning network, the study used a Convolution Neural Network and You Only Look Once algorithms. To obtain input data for analysis, a camera was mounted on the bonnet of the vehicle to obtain images of asphalt pavement and to mark the ground-truth cracks in the asphalt pavement image. In addition, an algorithm suitable for the automatic determination function of Deep Learning was proposed in order to calculate the crack ratio and crack rating. RESULTS: The result of analysis showed that the recall rate of cracks in this system was higher from FPPW 5.0E-06 to 96.03%. Furthermore, the accuracy of the grading system was found to be 100%, enabling the determination of very accurate ratings. The rate of processing per image was 0.4448 seconds on average, and the real-time analysis of pavement images presented no problem because the assessment took place within a short time. CONCLUSIONS : Applying this system to the pavement management system is expected to reduce the time required in finishing work and to determine a quantitative crack rating.