CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence

Existing manual detection methods have limitations, particularly for pavement cracks in complex backgrounds, which are manifested in low recognition accuracy, high misjudgment rate, and long time-consuming. To overcome these problems, artificial intelligence technology and charge-coupled devices (CCD) imaging technology are combined to construct an automatic identification method for pavement hidden cracks under complex background interference conditions. First, the classic semantic segmentation model U-net is selected as the basic model, and the MobileNet lightweight network is utilized to replace the encoder part of U-Net with huge parameters, to realize the lightweight of the model and improve the segmentation effect of pavement cracks. On this basis, the Atrous Channel Pyramid Attention (ACPA) mechanism is introduced into the U-net to further improve contextual information capability to focus on selectively relevant features. A pavement crack data set containing different complex and diverse crack types and background noise is used to evaluate the effectiveness and scope of application of the developed model. Quantitative evaluation results show that the developed model achieves an overall performance in the test set with a precision of 88.84%, recall of 89.76%, accuracy of 98.87%, and IoU of 89.95%, respectively. Combined with the analysis of the results of the comparison experiment and the ablation experiment, it can be inferred that the utilization of the MobileNet lightweight network to replace the encoder part of U-net can effectively construct a lightweight model while the ACPA module can effectively perform multi-scale and long-distance cross-channel interaction, help suppress useless features, strengthen useful features, and help the network learn stronger feature representations of hidden areas of pavement cracks.