Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection (1901.06340v2)

Abstract: 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 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 semantic information to low-level features for crack detection in a feature pyramid way. And, it balances the contribution of both easy and hard samples to loss by nested sample reweighting in a hierarchical way. To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods. Extensive experiments show that the proposed method outperforms these state-of-the-art methods in terms of accuracy and generality.

• The paper introduces FPHBN, a network that employs a feature pyramid and hierarchical boosting to enhance crack detection.
• The paper proposes the novel Average Intersection over Union (AIU) metric to address the limitations of traditional precision-recall measures.
• The paper demonstrates superior performance across five datasets, significantly outperforming existing edge detection and segmentation methods.

Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

The paper under review presents an innovative method for automatic pavement crack detection, aiming to address the challenges associated with manual inspections, which are typically laborious and inefficient. In light of recent advancements in deep learning, the authors propose the Feature Pyramid and Hierarchical Boosting Network (FPHBN), a network architecture poised to enhance the precision and efficacy of detecting pavement cracks.

Key Contributions

1. Network Architecture: FPHBN integrates a feature pyramid to incorporate contextual information from high-level to low-level layers, thus enhancing feature representation for crack detection. The hierarchical boosting mechanism is employed to differentiate between easy and hard samples, allowing for a balanced contribution during training.
2. Novel Measurement: The authors introduce the Average Intersection over Union (AIU) as a novel evaluation metric for crack detection, addressing the inadequacies of traditional precision-recall metrics when applied to crack detection scenarios with varying widths and annotation biases.
3. Evaluation and Results: The proposed method is rigorously evaluated across five crack datasets, demonstrating superior performance in terms of accuracy and generalizability compared to state-of-the-art methods, including edge detection and semantic segmentation techniques.

Detailed Analysis

Feature Pyramid Integration:

By leveraging a top-down architecture to build a feature pyramid, the FPHBN introduces context from higher to lower-level feature maps. This integration addresses the limitations of previous models that suffered from inadequate feature representation in lower layers, thereby improving the detection of cracks with complex topologies and varying intensities.

Hierarchical Boosting Mechanism:

The hierarchical boosting strategy reweights samples to focus on more challenging examples, mitigating the skewed distribution typically observed in crack and non-crack samples. This approach facilitates efficient parameter learning and enhances model robustness.

Comparative Performance:

The FPHBN outperforms existing methods, including HED and RCF, by demonstrating improved ODS and AIU scores across multiple datasets. Notably, it shows significant gains on the CRACK500 dataset, which is deemed to have complex backgrounds and diverse crack patterns.

Implications and Future Directions:

The advancements shown by FPHBN highlight the potential for developing even more sophisticated models that further leverage hierarchical structures and context integration. The AIU metric could become a standard for evaluating methods in scenarios where the shape and structure of interest (e.g., cracks, edges) are intricate. Future work could explore real-time deployment possibilities and enhancements in feature extraction from complex backgrounds.

The paper stands as an important contribution to the field of automated infrastructure inspection, with implications extending beyond mere crack detection to broader applications within intelligent transportation systems and smart city initiatives. Future research should also investigate the adaptation of the proposed methodology to diverse environmental conditions, potentially using augmented datasets.