Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.

The self-attention mechanism has an excellent ability to capture long-range dependencies of data. To enable the self-attention mechanism in point cloud tasks to focus on both local and global contexts, we design a separable self-attention mechanism for point clouds by decomposing the construction of the attention map of a point cloud into two steps: Intra-patch Attention and Inter-patch Attention, the former computes the attention map of the tokens corresponding to each point in the local patch of the point cloud for mining local fine-grained semantic relationships, while the latter constructs the attention map among all the patches for mining long-distance interaction information. The two self-attention mechanisms work in parallel, focusing on both fine-grained local patterns and considering global scenes. Equipped with Intra-patch Attention and Inter-patch Attention modules, we construct a hierarchical end-to-end point cloud analysis architecture called Separable Transformer and conduct exhaustive experiments to demonstrate that the performance of the network proposed in this paper is highly competitive with state-of-the-art methods.

Remote sensing image change detection (CD) refers to the automated or semi-automated detection of differences between two remote sensing images taken at different times in the same region. To achieve better global modeling and faster inference, we propose a network architecture containing a hierarchical swin transformer block and deformable attention transformers crossed for encoding and lightweight MLP decoding to solve the CD task. The deformable attention transformer allows adaptive adjustment of the relationships and weights between feature mappings to effectively combat variations and noise interference in various scenes. The alternating use of swin transformer block and deformable attention transformer ensures the efficiency as well as the flexibility of the model. The lightweight MLP approach provides better ability to extract spatial features and contextual information, as well as faster inference speed. Compared with other methods, our proposed DAHT-Net method improves F1 scores by 0.98 and 2.61 on LEVIR-CD, CDD and two publicly available benchmark datasets, respectively, and performs well on other measures. These experimental results validate that the DAHT-Net network outperforms other comparative methods and highlight its effectiveness in remote sensing image change detection. In summary, our proposed hierarchical deformable attention-guided transformer network model provides a promising solution for remote sensing image change detection with superior performance compared to other state-of-the-art methods.

Edge detection is a basic problem in computer vision and image processing. The main purpose of edge detection is to identify points with obvious brightness changes in digital images. At present, there are many good detection methods, but most of them do not consider the correctness and crispness of edges at the same time. In order to address this problem, this paper proposes a method based on a deep convolution neural network. The method is mainly based on a dense network that is then combined with the single network structure of a backward refinement path module. The former can detect and retain the feature information between different layers in an image. The latter makes full use of the extracted information so that the low-level detail features and high-level abstract features can be better integrated in the final output. We tested the method on the BIPED data set. The results show that the correctness and crispness of the edges can be balanced in the detection process, and the ODS, OIS and AP of this method reach 0.888, 0.893 and 0.916, respectively. Compared with the state-of-the-art approaches, the proposed method improves the standard evaluation by 3%-5%, and the convergence speed is also significantly improved.

An uncertain random variable is a tool used to research indeterminacy quantities involving randomness and uncertainty. The concepts of an ’uncertain random process’ and an ’uncertain random renewal process’ have been proposed in order to model the evolution of an uncertain random phenomena. This paper designs a new uncertain random process, called the uncertain random delayed renewal process. It is a special type of uncertain random renewal process, in which the first arrival interval is different from the subsequent arrival interval. We discuss the chance distribution of the uncertain random delayed renewal process. Furthermore, an uncertain random delay renewal theorem is derived, and the chance distribution limit of long-term expected renewal rate of the uncertain random delay renewal system is proved. Then its average uncertain random delay renewal rate is obtained, and it is proved that it is convergent in the chance distribution. Finally, we provide several examples to illustrate the consistency with the existing conclusions.