Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with a similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), for pavement crack detection. The proposed network integrates context information to low-level features for crack detection in a feature pyramid way, and it balances the contributions of both easy and hard samples to loss by nested sample reweighting in a hierarchical way during training. In addition, we propose a novel measurement for crack detection named average intersection over union (AIU). To demonstrate the superiority and generalizability of the proposed method, we evaluate it on five crack datasets and compare it with the state-of-the-art crack detection, edge detection, and semantic segmentation methods. The extensive experiments show that the proposed method outperforms these methods in terms of accuracy and generalizability. Code and data can be found in https://github.com/fyangneil/pavement-crack-detection.

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.

In data-driven deep learning-based modeling, data quality may substantially influence classification performance. Correct data labeling for deep learning modeling is critical. In weakly-supervised learning, a challenge lies in dealing with potentially inaccurate or mislabeled training data. In this paper, we proposed an automated methodological framework to identify mislabeled data using two metric functions, namely, Cross-entropy Loss that indicates divergence between a prediction and ground truth, and Influence function that reflects the dependence of a model on data. After correcting the identified mislabels, we measured their impact on the classification performance. We also compared the mislabeling effects in three experiments on two different real-world clinical questions. A total of 10,500 images were studied in the contexts of clinical breast density category classification and breast cancer malignancy diagnosis. We used intentionally flipped labels as mislabels to evaluate the proposed method at a varying proportion of mislabeled data included in model training. We also compared the effects of our method to two published schemes for breast density category classification. Experiment results show that when the dataset contains 10% of mislabeled data, our method can automatically identify up to 98% of these mislabeled data by examining/checking the top 30% of the full dataset. Furthermore, we show that correcting the identified mislabels leads to an improvement in the classification performance. Our method provides a feasible solution for weakly-supervised deep learning modeling in dealing with inaccurate labels.