Real-time detection and geometric analysis algorithm for concrete cracks based on the improved U-net model

Abstract

Aiming at complex operation problems, low precision and poor robustness of traditional concrete crack detection methods, a real-time concrete crack detection and geometric analysis algorithm based on the improved U-net model is proposed. First, the efficient channel attention (ECA) module is embedded in the U-net model to reduce the loss of target information. The DenseNet network is used instead of the VGG16 network in the U-net basic model architecture, making transmitting features and gradients more effective. Then, based on the improved U-net model, the concrete crack detection experiment is performed. The experimental results indicate that the improved U-net model has 91.56% pixel accuracy (PA), 80.12% mean intersection over union (mIoU), 84.89% recall and 88.10% F1_score. The mIoU, PA, recall and F1_score of the improved U-net model increased by 17.39%, 7.82%, 2.62% and 5.10%, respectively, compared with the original model. Next, the real-time detection experiment of concrete cracks is performed based on the improved U-net model. The FPS of the improved model is the same as that of the original model and reaches 42. Finally, the geometric analysis of concrete cracks is performed based on the detection results of the improved U-net model. The area, density, length and average width information of concrete cracks are effectively extracted. The research results indicate that the detection effect of this study’s model on concrete cracks is considerably improved and that the model has good robustness. The model proposed in this study can achieve intelligent real-time and accurate identification of concrete cracks, which has broad application prospects.