A Novel SegNet Model for Crack Image Semantic Segmentation in Bridge Inspection

Cracks on bridge surfaces represent a significant defect that demands accurate and efficient inspection methods. However, current approaches for segmenting cracks suffer from low accuracy and slow detection speed, particularly when dealing with fine and small cracks that occupy only a few pixels. In this work, we propose a novel crack image semantic segmentation method based on an enhanced SegNet. The proposed approach addresses these challenges through three key innovations. First, we reduce the network depth to improve computational efficiency while maintaining accuracy. Furthermore, we employ ConvNeXt-V2 to effectively extract and fuse crack features, thereby improving segmentation performance. To handle pixel imbalance during loss calculation, we integrate the Dice coefficient into the original cross-entropy loss function. Experimental results demonstrate that our enhanced SegNet achieves remarkable improvements in mIoU for non-steel and steel crack segmentation tasks, reaching 82.37% and 77.26%, respectively. Our approach outperforms state-of-the-art methods in both inference speed and accuracy.