Bridge Crack Detection Based on Improved DeeplabV3+ and Migration Learning

Cracks are one of the most important diseases of bridges. Timely and efficient detection and evaluation of cracks is very important to maintain the health of bridges. Aiming at the high integration cost and low detection accuracy of fracture annotation data, an improved DeeplabV3+ model based on attention mechanism and transfer learning is proposed. The model adds attention mechanism to obtain rich context information, improve the learning ability of crack feature channel and reduce the influence of background noise and other features. Then, through the combination of public data set and small sample data set, the source domain data set and target domain data set are established for migration learning, so as to reduce the impact of too few training samples on detection performance. The experimental results show that the improved DeeplabV3+ model has achieved good detection effect on bridge crack detection, and the detection accuracy has reached 93.3%, which is 3 percentage points higher than the original model. The transfer learning training model achieves high detection accuracy on small sample data, which can save a lot of labeling costs. © The Author(s) 2024.