A Novel Applicable Shadow Resistant Neural Network Model for High-Efficiency Grid-Level Pavement Crack Detection

Abstract

To address two key challenges—limited grid-level detection capability and difficulty in detecting pavement cracks in complex environments, this study proposes a novel neural network model called CrackcellNet. This innovative model incorporates an output structure that enables end-to-end grid recognition and a module that enhances shadow image data to enhance crack detection. The model relies on the design of consecutive pooling layers to achieve adaptive target size grid output. By utilizing image fusion techniques, it enhances the quantity of shadow data in road surface detection. The results of ablation experiments indicate that the optimal configuration for CrackcellNet includes V-block and shadow augmentation operations, dilation rates of 1 or 2, and a convolutional layer in the CBA module. Through extensive experimentation, we have demonstrated that our model achieved an accuracy rate of 94.5% for grid-level crack detection and a F1 value of 0.839. Furthermore, practical engineering validation confirms the model's efficacy with an average PCIe of 0.045, providing valuable guidance for road maintenance decisions.