Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection

Pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. To precisely localize crack from an image, it is critical to effectively extract and aggregate multi-granularity context, including the fine-grained local context around the cracks (in spatial-level) and the coarse-grained semantics (in semantic-level). In this paper, we apply the dilated convolution as the backbone feature extractor to model local context, then we build a context guidance module to leverage semantic context to guide local feature extraction at multiple stages. To handle label alignment between stages, we apply the Multiple Instance Learning (MIL) strategy to align the feature between two stages. In addition, to our best knowledge, we have released the largest, most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The experimental results on the three crack datasets demonstrate that the proposed method performs well and outperforms the current state-of-the-art methods. On BPC, the proposed model achieved AP 88.32% with the 16.89 M parameters under the 45.36 GFlops runing speed. Datset and code are publicly available at: https://github.com/pangjunbiao/BPC-Crack-Dataset.