Crack Detection Using Deep Supervised Networks with Multi-scale and Multi-channel Feature Fusion
A fully convolutional network(FCN)segmentation network based on VGG-16 skeleton and fused with deep features was proposed to address the problem of shallow convolutional layers extracting a large amount of noise while extracting some detailed information when using convolutional neural networks to extract wide cracks.Side outputs are added to each layer to directly supervise the model's learning of more useful information.Furthermore,a Focal Loss function was adopted to address the issue of imbalanced classification of positive and negative samples in the dataset itself.This fusion application of multi-scale and multi-channel deep features with unique loss functions enables the network to have strong anti-interference ability and fast convergence speed.On the DeepCrack dataset,the proposed deep feature fusion network(DFFN)exhibits better performance and faster inference speed compared to holistically nested edge detection(HED),FCN and DeepCrack.
万方数据
维普
