Traditional Digital Image Correlation (DIC) methods face challenges in terms of computational speed, especially for large datasets, and in handling complex scenarios involving high-frequency deformation or discontinuities such as cracks. With the advent of deep learning, the potential for leveraging Convolutional Neural Networks (CNNs) for DIC has become increasingly apparent. Deep learning has revolutionized computer vision, achieving state-of-the-art results in tasks such as image classification, object detection, and segmentation. The success of CNNs in these domains suggests that they could also be applied to the task of DIC, potentially offering improvements in both accuracy and computational efficiency.In this context, the publication presents a novel approach to DIC using an advanced CNN architecture known as UNet++. The proposed method, termed DIC-Net++, is designed to address the limitations of traditional DIC algorithms and enhance the performance of deep learning in the context of speckle image deformation measurement. The article has developed two specialized networks within the DIC-Net++ framework: DIC-Net++-d for displacement field measurement and DIC-Net++-s for strain field measurement. These networks are built upon the UNet++ architecture, which is known for its effective feature extraction and fusion capabilities, and have been augmented with residual blocks and coordinate attention mechanisms to improve their performance.To facilitate the training of these networks and enhance their generalization capabilities, a new dataset is constructed that extends the Hermite dataset with additional real experimental speckle patterns and variations in brightness. This comprehensive dataset includes 47 800 image pairs, with 35 800 pairs derived from the Hermite dataset, 10 000 from real experiments, and 2 000 involving high-frequency deformation. The dataset is meticulously divided into training, validation, and testing subsets to ensure a robust evaluation of the proposed networks.The text describes the design and training process of the DIC-Net++ networks, emphasizing the innovative features of the architecture and the training procedures. A loss function based on the Average Endpoint Error (AEE) is utilized, which quantifies the vector difference between the predicted and actual displacement or strain field values. The networks are trained using the AdamW optimization method with a weight decay rate of 5×10^-4, and a constant learning rate of 5×10^-4 to prevent overfitting. The batch size is set to 32 to balance the trade-off between GPU memory usage and training speed.The results of the experiments conducted using the proposed DIC-Net++ method are comprehensively presented and analyzed. The article has compared the performance of DIC-Net++-d with other competing networks, such as StrainNet-f and DIC-Net-d, on both self-built and public datasets. The experiments include tests on datasets with artificially introduced brightness variations to assess the robustness of the networks to such changes. The findings indicate that DIC-Net++-d outperforms other networks in terms of both accuracy and robustness, with minimal degradation in performance even when the datasets include significant brightness variations.Furthermore, the article has evaluated the performance of DIC-Net++ on the DIC Challenge datasets, which are standardized datasets designed to provide a fair comparison of different DIC algorithms. The results on the Star5 and Star6 image sets demonstrate that DIC-Net++-d achieves the lowest Metrological Efficiency (MEI) among the tested networks, indicating superior accuracy and resolution. The MEI values for DIC-Net++-d on the Star5 and Star6 datasets are 1.372 and 0.003 7, respectively, which are significantly lower than those of other networks.This paper accelerates the application of deep learning technology in the field of DIC through the DIC-Net++ method. The integration of residual blocks and coordinate attention mechanisms into the network design has proven to be highly effective in enhancing the network's ability to extract and fuse features, leading to improved measurement accuracy and robustness. The extensive experiments and comparisons with existing methods demonstrate the superior performance of DIC-Net++, both on self-built datasets and public benchmarks.