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Currently, cracks are the most common defect in pavement diseases. Long-term non-maintenance can lead to crack lengthening and expansion, causing serious traffic accidents, as well as shortening the service life of pavement cracks. Therefore, it is of utmost importance to maintain cracks at an early stage. Due to the effect of some challenging factors, such as various shape information of the cracks, complex textured backgrounds, light shadows, similar texture objects, micro cracks and other factors, accurate crack detection still faces a certain challenges. To solve the above problems, a dual-encoding multi-scale fusion network based on the combination of convolutional neural network (CNN) and transformer network is proposed, named DMF-Net. To obtain stronger feature representations, a dual-encoding path is built to acquire global context features and local detail information simultaneously, where global context features are extracted based on the transformer branch, and the local detail features are extracted based on the CNN branch to detect tiny details of the cracks. Meanwhile, an interactive attention learning (IAL) module is introduced to effectively fuse the global features from the transformer branch and the local detail information from the CNN branch, achieving mutual communication and learning of different feature information. In addition, to enrich the feature representation ability, an attention-based feature enhancement (AFE) module is introduced to acquire more global contexts. Furthermore, faced with the crack detection task with class imbalance issue, a triple attention module (TAM) is built to emphasize the micro cracks. Finally, in the segmentation prediction stage, the deep supervision mechanism is also introduced to accelerate the convergence speed of the model, and serve effective multi-scale feature fusion. Compared with the current mainstream segmentation models, excellent performance has been obtained, which could provide a feasible scheme for the early maintenance of pavement cracks. The source code about proposed DMF-Net is available at https://github.com/Bsl1/DMFNet.git.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent’s time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach’s effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

This article investigates the information congestion problems for nonlinear discrete-time multi-input–multi-output multiagent systems (MASs) with fading channels when executing formation tasks. We first establish a virtual linear data model with a time-varying pseudo-Jacobian matrix variable for the MASs, which is independent of the dynamics model. Then, we formulate an event-triggered control scheme and a predictive compensation method to alleviate the communication burden and information congestion effects, respectively. Moreover, we propose two formation schemes for the MASs, considering limited communication resources, fading channels, and random delays to perform formation control and bipartite formation control tasks. The convergences of these two control protocols are strictly proved. Finally, simulations and hardware tests are conducted to verify the effectiveness of the proposed strategies.

In the early prevention stage of colorectal cancer (CRC), the utilization of automatic polyp segmentation techniques from colonoscopy images has demonstrated efficacy in mitigating the misdiagnosis rate. Nonetheless, accurate polyp segmentation is always against with various challenges, including the presence of inconsistent size and morphological changes within polyp classes, limited interclass contrast, and high levels of interference. In recent years, much methodologies based on convolutional neural networks (CNNs) have been widely introduced to enhance the precision of polyp segmentation. However, two significant hurdles persist: 1) these methods frequently suffer from an inadequate acquisition of contextual features, causing insufficient feature representation and 2) there is a deficiency in recognizing intricate information, such as precise polyp boundaries. Addressing these issues, this article introduces a novel dual-branch multiattention network, denoted as DBMA-Net. Specifically, proposed DBMA-Net primarily introduces a dual-encoding path that combines CNN and Transformer-based approaches to enrich feature representation. Additionally, an attention-based fusion module (AFM) is incorporated between the dual-encoding path, aimed at optimizing features by supplementing local information with global insights. Subsequently, two distinct attention mechanisms are introduced to enhance features: the attention-based enhancement module (AEM) and the multiview attention module (MAM), to acquire stronger local features. These modules serve to enrich the finer details while extensively exploring and enhancing the lesion region, thereby further elevating segmentation accuracy. Following the above feature optimization, the enhanced feature maps are hierarchically integrated across multiple scales based on the proposed multiscale feature integration module (MFIM) for accurate feature reconstruction. This strategy not only curtails feature loss but also aids in restoring feature resolution. Ultimately, comprehensive experiments, including comparative and ablation studies across various datasets, validate the superior segmentation performance of the proposed network compared to most state-of-the-art (SOTA) models.

Automatic crack detection remains challenging due to factors such as irregular crack shapes and sizes, uneven illumination, complex backgrounds, and image noise. Deep learning has shown promise in computer vision for pixel-wise crack detection, but existing methods still suffer from limitations such as information loss, insufficient feature fusion, and semantic gap issues. To address these challenges, a novel pavement crack segmentation network, called PAF-Net, is proposed, which incorporates progressive and adaptive feature fusion. To mitigate information loss caused by feature downsampling, a progressive context fusion (PCF) block is introduced to capture context information from adjacent scales. To better capture strong features from local regions, a dual attention (DA) block is proposed that leverages both global and local context information, reducing the semantic gap issue. Furthermore, to achieve effective multi-scale feature fusion, a dynamic weight learning (DWL) block is proposed that enables efficient fusion of feature maps from different network layers. Additionally, a multi-scale input unit is incorporated to provide the proposed segmentation network with more contextual information. To evaluate the performance of PAF-Net, we conduct experiments using four common evaluation metrics and compare it with multiple mainstream segmentation models on three public datasets. The proposed PAF-Net demonstrates superior segmentation accuracy for pixel-level crack detection compared to other segmentation models, as evident from qualitative and quantitative experimental results.