Evaluation and optimization of image segmentation models in pavement crack detection

Pavement cracks are a critical aspect of road maintenance, and their timely and accurate detection is essential for road upkeep. This study aims to evaluate and optimize the application of image segmentation models in pavement crack detection to enhance the efficiency and accuracy of road maintenance. The research methods include experimental comparisons of five mainstream image segmentation models (U-Net++, MANet, FPN, LinkNet, and PAN), and further improvement of model performance through hyperparameter optimization. The main results indicate that the U-Net++ model performs best in terms of the Dice coefficient, with an average Dice score of 0.715 when the batch size is 32. After optimization of the encoder, optimizer, and learning rate, the final Dice coefficient increased to 0.734. The analysis shows that the superior performance of the U-Net++ model is attributed to its improved skip connections and cascade modules, as well as its effective feature fusion capability.