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The maintenance of pavements takes considerable time and poses a significant task, especially when it comes to detecting cracks at the pixel level. Due to the complexity of pavement conditions, such as road markings, shadows, and oil stains, deep learning techniques are still a challenge in automating crack detection. This paper presents a novel methodology termed as CrackHAM, which is an encoder-decoder network founded on the U-Net architecture. The primary objectives of CrackHAM are twofold: to achieve accurate and robust pavement crack detection while reducing the parameters of the network. Our study introduces two significant improvements to the existing neural network architecture, namely the phased multi-fusion module and the dual attention mechanisms. These improvements improve the process of defect extraction, resulting in an improved level of performance. Furthermore, a novel module named HASPP is devised to augment the network&#x2019;s capacity to acquire more comprehensive receptive fields. In order to lower the number of network parameters, a technique is employed whereby only use half of the number of input channels and output channels in the VGG16 are utilized as U-Net encoder modules. The empirical findings demonstrate that in the Deepcrack, Crack500, and FIND public datasets, CrackHAM achieves superior segmentation performance compared to the FCN, Deeplabv3, Swin-Unet, and U-Net models while utilizing only one-third of the computational resources.

CrackHAM: A novel automatic crack detection network based on U-Net for asphalt pavement