Computer vision-based Road Crack Detection Using an Improved I-UNet Convolutional Networks

Cracks are one of the common and important diseases on the pavement surface. The traditional crack detection method mainly relies on manual operation, which is time-consuming and laborious in practical treatment. Therefore, an improved road cracks algorithm based on the I-UNet is proposed in this paper. In the method, the dilated convolution is used to expand the receptive field of the convolution. The method likes "Inception" is used to extract different scales of image features and conduct multi scale feature fusion, the Elu activation function is used. After training, I-UNet can efficiently segment complete cracks in complex environments. Experiments show that the proposed I-UNet based road crack detection method is more robust and more accurate than the U-Net based method.