An attention-based progressive fusion network for pixelwise pavement crack detection

Abstract

Detecting pavement cracks is a complex task in pavement maintenance. Convolutional neural network (CNN)-based approaches have been increasingly used for this purpose in recent years, but these approaches still have limitations, such as insufficient processing of local features, poor boundary detection ability and information loss issue. In this paper, a novel CNN-based model, named APF-Net, is presented. Specifically, to extract richer and more feature information, proposed model utilizes a progressive fusion module (PF) that enables effective feature enhancement by feature learning from cross-layer features, which operates progressively across adjacent network layers, enabling the crack segmentation model to extract more global and detail features from the crack images. Additionally, a hybrid multiple attention (HMA) module that incorporates both a spatial attention mechanism from two directions and a channel attention mechanism, is proposed, which could effectively capture the long-term dependence of crack features and enhance crack boundary detection. Moreover, proposed model employs a lightweight feature extraction block, named mobile inverted bottleneck convolution (MBC) block, which significantly reduces the model parameters and enables more efficient processing of the crack images. This approach results in a reduction of the computational load and faster detection of pavement cracks. The effectiveness and generalization of the proposed APF-Net have been evaluated through experiments on three publicly available crack datasets. The results demonstrate that the proposed model outperforms other state-of-the-art segmentation models in detecting pavement cracks while with 7.6M parameters and 5.0G FLOPs. Our proposed model can contribute to the development of automatic pavement crack detection systems, which can aid in the maintenance and preservation of roads and highways. The source code for proposed APF-Net is available at https://github.com/MMYZZU/APFNet.git.