A novel MO-YOLOv4 for segmentation of multi-class bridge damages

Abstract

Most studies of multi-class damage segmentation overlook two prominent and critical challenges: the substantial variations in damage scale and the susceptibility of network training to background interferences. As a consequence, achieving the desired performance in multi-class damage segmentation becomes a difficult task. To tackle these challenges, a novel multi-class damage segmentation method, MO-YOLOv4, is proposed based on the widely used YOLOv4 structure. MO-YOLOv4 incorporates the Multi-Scale Attention (MSA) module to greatly enhance the feature extraction ability for multi-class damages, and employs the Oriented Bounding Box (OBB)-based segmentation strategy for effectively reducing the influence of background interferences during the network training phase. The effectiveness of the MSA module and OBB-based segmentation strategy is validated through verification and ablation experiments. In the comparative study, the Mean Precision rate (MP), Mean Recall rate (MR), Mean F1-score (MF) and Mean Intersection over Union rate (MIoU) of the proposed MO-YOLOv4 reach up to 82.49%, 72.73%, 77.04% and 64.28%, respectively, indicating the superior level of segmentation performance offered by MO-YOLOv4 in comparison to the mainstream semantic segmentation methods for multi-class damages.