Bridge crack segmentation and measurement based on SOLOv2 segmentation model

Abstract

With the continuous increase of vehicular traffic, the safety caused by bridge crack damage is becoming increasingly prominent. Bridge crack analysis and measurement are of great significance for promoting road traffic safety. However, existing bridge crack image segmentation methods have shortcomings in processing image detail features, resulting in the inability to better measure the actual size of bridge cracks. Therefore, to further optimize the calculation method, a bridge crack image segmentation method based on improved SOLOv2 is designed to achieve more accurate bridge image segmentation. Based on the image segmentation results and combined with the skeleton data extraction method, a bridge crack calculation method is designed. From the results, the segmentation accuracy for crack images was 92.05 % and 93.57 %, respectively. The average mIoU of AM-SOLOv2 method was 0.75, significantly lower than commonly used crack image segmentation methods. In addition, the mIoU value variation amplitude of the AM-SOLOv2 method was relatively smaller. The crack length and width errors were within 0.05 mm and 0.06 mm, significantly lower than the comparison method. It indicates that this method can achieve more accurate crack image segmentation and calculation. This is beneficial for a deeper understanding of the performance degradation and crack damage evolution of bridge structures, thereby improving bridge design and construction technology.