A Road Crack Detection Model Integrating GLMANet and EFPN

Abstract

Detecting road cracks is crucial for ensuring road traffic safety and stability. However, currently existing detection methods do not usually pay close attention to the global, local and multi-scale feature information of road crack images, resulting in poor detection effects. To overcome this limitation, we propose a road crack detection model that combines the global and local multi-scale attention network (GLMANet) and enhanced feature pyramid network (EFPN). The GLMANet can effectively extract useful global, local and multi-scale feature information of crack images through a novel global and local multi-scale attention mechanism. Meanwhile, EFPN utilizes global adaptive attention module and multi-receptive field feature enhancement module to mitigate information loss during feature map generation, enhancing the representation of advanced semantic features in the feature pyramid. Leveraging faster region-based convolutional neural networks (Faster R-CNN) as the object detection architecture, we conduct experimental evaluations on both publicly available crack datasets and a dataset we collected. The proposed model achieved optimal detection performance across all three datasets. In comparison experiments with the current state-of-the-art crack detection methods, the proposed model outperformed other models, with AP and AP50 increasing by up to 16% and 11%, respectively, validating its effectiveness and superiority. Additionally, the model achieved a good balance between complexity and detection performance with 79.2 GFLOPs and 72.60 million parameters.