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.