The crack detection model for transportation infrastructure based on deep learning relies on large-scale data for training. To address the problem of limited availability of diverse crack samples in specific transportation facility scenarios, this paper proposed a transportation infrastructure crack image augmentation method based on Pix2PixHD model. Initially, the Pix2PixHD model was used to establish a spatial mapping relationship between real crack images and annotated labels based on a small amount of collected crack image data. Subsequently, the objects in the label domain were edited to generate crack contours representing various forms, using methods such as label transfer from other datasets, manual editing, morphological dilation operations, and random superimposition operations. Finally, the edited label domain was transformed back to the image domain using the Pix2PixHD model, so as to achieve an adaptive augmentation of the transportation infrastructure crack dataset. This paper considered major materials and structures in transportation infrastructure (asphalt pavement, tunnel linings, and concrete structures) and conducted experiments using the GAPS384, Tunnel200, and DeepCrack datasets. Results demonstrate that the U-Net model trained on the augmented dataset achieves higher detection accuracy and is more likely to avoid local optima. Compared to the DCGAN model, the proposed method effectively controls crack morphology while maintaining the continuity of the crack skeleton, thereby enhancing the morphological diversity of the original crack dataset and improving the generalization capability of the detection model in specific transportation infrastructure scenarios.

The 4-dimensional CT (4D-CT) images of the lungs undergo large deformations due to respiration and heartbeat, and the scale of motion within the lungs may be larger than the structures of interest (blood vessels, airways, etc.) that the algorithm uses for the optimization process, which may result in the registration algorithms only aligning the obvious features such as blood vessels and airways. To address the problem of high variability of the aligned intensities for structures with large deformations such as the lung parenchyma contour, this paper proposed a multi-scale residual deformable lung CT image alignment algorithm framework based on unsupervised end-to-end deep learning. A multi-scale deep residual network in the form of an encoder-decoder structure was used as a generative model for the deformation field in the proposed registration framework, so as to enhance the feature representation, to increase the effective parameter utilization efficiency parameters and effectively improve the convergence ability of the network. A multi-resolution self-attentive fusion module was used to improve the network’s ability to perceive multi-scale information. And a hopping connection containing a feature correction extraction module was designed to selectively extract the feature maps output by the encoder and recalibrate them for the decoder to learn the alignment offsets. Finally, this paper compared the proposed alignment algorithm with traditional algorithms and the current state-of-the-art unsupervised alignment algorithms on the Dir-lab public dataset. The results show that, the target alignment error of the proposed registration algorithm framework on the Dir-lab public dataset can reach 1.44 mm ± 1.24 mm, which is better than traditional algorithms and the mainstream unsupervised alignment algorithm. In addition, the estimation of the dense deformation vector field takes less than 2.00 s with the control folding voxel less than 0.1%, indicating the great potential of the algorithm in studying time-sensitive lungs.

The integer transform methods are widely adopted in international image and video coding standards because of its fast calculation speed. The existing integer transform methods based on the continuous orthogonal function system not only struggle to obtain the exact integer form of the original transform, but also fails to overcome the Gibbs oscillation phenomenon in the discontinuous signal representation, thus reduces the reconstructed image quality. This paper proposed a new integer transform algorithm and its image compression method based on discontinuous orthogonal U-system. Firstly, the piecewise integration and the Gram-Schmidt process were used to calculate the two-dimensional orthogonal matrix of the U-system, and the scaling factors of row vectors were extracted to obtain the integer matrix. Secondly, the reversible integer U transform was established and the integer matrix was applied to concentrate the energy of images into a small amount of data sets, while merging scaling factors with quantization to reduce computational burden. Then, the fast integer U transform was achieved by using matrix decomposition and sparse matrices. Finally, the integer U transform module and inverse transform module were designed to alleviate the pressure of image storage and transmission. Experimental results show that the proposed method can reduce truncation errors of reversible image transform compared with related algorithms; the new method obtains higher compression image quality in image and video compression experiments, and the fast transform algorithm effectively saves computational time.

Surface defect detection is an important part of the modern industrial production process. The existing visual defect detection methods generally achieve detection by analyzing a single RGB or grayscale image of the target object and using differential features between the defect and the background. They are suitable for objects with a large difference between the target and the background, such as the detection of metal surface oxidation and spot defects. However, the simple RGB image cannot effectively characterize the 3D defect features such as dents and bulges, which are mainly formed by depth changes, ultimately resulting in missed detection. To this end, this paper extracted the 3D geometric appearance information of the object surface to be tested according to multi-directional light imaging and photometric stereo principle. Next, the original multi-directional light images were effectively fused using the contrast pyramid fusion algorithm to obtain the enhanced 2D RGB fusion image features of the defects. Then, on the basis of the multi-target detection framework YOLOv5, with the above geometric appearance and RGB fusion images as inputs, a defect detection network model based on dual stream feature fusion detection network model was constructed. The model introduces the spatial channel attention residual module and the gated recurrent unit (GRU) feature fusion module and is able to organically fuse the different modal features at multiple levels to realize the effective extraction of the 2D RGB and 3D appearance information of the surface defects, so as to achieve the purpose of dealing with the detection of 2D and 3D defects at the same time. Finally, the detection experiments were conducted on the surface defects of several typical industrial products. The results show that mAP of the method in the paper is above 90% on several datasets, and it can simultaneously cope with the detection of 2D and 3D defects, so the detection performance is better than that of the current mainstream methods, and it can meet the detection requirements of different industrial products.