Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

•Pre-trained deep Convolutional Neural Networks (DCNN) were used for crack detection.•Pavement images sampled from the FHWA/LTPP database were used as datasets.•The truncated VGG-16 DCNN was used as a deep feature generator for road images.•Various machine learning classifiers were trained using the semantic image vectors.•A neural network classifier trained on deep transfer learning vectors gave the best results.

Automated pavement distress detection and classification has remained one of the high-priority research areas for transportation agencies. In this paper, we employed a Deep Convolutional Neural Network (DCNN) trained on the 'big data' ImageNet database, which contains millions of images, and transfer that learning to automatically detect cracks in Hot-Mix Asphalt (HMA) and Portland Cement Concrete (PCC) surfaced pavement images that also include a variety of non-crack anomalies and defects. Apart from the common sources of false positives encountered in vision based automated pavement crack detection, a significantly higher order of complexity was introduced in this study by trying to train a classifier on combined HMA-surfaced and PCC-surfaced images that have different surface characteristics. A single-layer neural network classifier (with 'adam' optimizer) trained on ImageNet pre-trained VGG-16 DCNN features yielded the best performance.