Asphalt pavement cracks constitute a prevalent and severe distress of surfacing materials and before selecting the appropriate repair strategy, the type of deterioration must be classified to identify root causes. Efficient detection and classification minimize concomitant costs and simultaneously increase pavement service life. This study adopts convolutional neural networks (CNN) for asphalt pavement crack detection using secondary data available via the CRACK500 dataset and other datasets provided by GitHub. This dataset had four types of cracks viz.: horizontal, vertical, diagonal and alligator. Five pre-trained CNN models trained by ImageNet were also trained and evaluated for transfer learning. Emergent results demonstrate that the EfficientNet B3 is the most reliable model and achieved results of 94% F1_Score and 94% accuracy. This model was trained on the same dataset by performing transfer learning on pre-trained weights of ImageNet and fine-tuning the CNN. Results revealed that the modified model shows better classification performance with 96% F1_Score and 96% accuracy. This high classification accuracy was achieved by a combination of effective transfer-learning of ImageNet weight and fine-tuning of the top layers of EfficientNet B3 architecture to satisfy classification requirements. Finally, confusion matrices demonstrated that some classes of cracks performed better than others in terms of generalization. Further additional advancement with fine-tuned pre-trained models is therefore required. This study showed that the high classification results resulted from using a successful transfer learning of ImageNet weights, and fine-tuning.