Bridge Crack Identification Based on Feature Fusion of Convolutional Neural Networks

Abstract

Aiming at the problem of low image quality of bridge cracks and poor identification of bridge cracks by single convolutional neural network method, this paper proposes a bridge crack identification algorithm based on feature fusion of convolutional neural networks. The algorithm first augments the image to obtain enough training samples to simulate the real scene. Then, using super-resolution technology to reconstruct the image size to enhance the image details; then, using AlexNet and VGG11 to construct and train the bridge crack feature fusion model. Finally, the SoftMax classifier is used to classify and identify the merged features to obtain the crack detection results. The experimental results show that the proposed algorithm is better than the single AlexNet and VGG11 feature extraction, and the image recognition accuracy is increased by 0.32% and 0.41% respectively. The loss value is smaller under the same iteration number, and the improved algorithm is better.