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Bridge detection from Synthetic Aperture Radar (SAR) images has very important strategic significance and practical value, but there are still many challenges in end-to-end bridge detection. In this paper, a new deep learning-based network is proposed to identify bridges from SAR images, namely, multi-resolution attention and balance network (MABN). It mainly includes three parts, the attention and balanced feature pyramid (ABFP) network, the region proposal network (RPN), and the classification and regression. First, the ABFP network extracts various features from SAR images, which integrates the ResNeXt backbone network, balanced feature pyramid, and the attention mechanism. Second, extracted features are used by RPN to generate candidate boxes of different resolutions and fused. Furthermore, the candidate boxes are combined with the features extracted by the ABFP network through the region of interest (ROI) pooling strategy. Finally, the detection results of the bridges are produced by the classification and regression module. In addition, intersection over union (IOU) balanced sampling and balanced L1 loss functions are introduced for optimal training of the classification and regression network. In the experiment, TerraSAR data with 3-m resolution and Gaofen-3 data with 1-m resolution are used, and the results are compared with faster R-CNN and SSD. The proposed network has achieved the highest detection precision (P) and average precision (AP) among the three networks, as 0.877 and 0.896, respectively, with the recall rate (RR) as 0.917. Compared with the other two networks, the false alarm targets and missed targets of the proposed network in this paper are greatly reduced, so the precision is greatly improved.

As sea-crossing bridges are important hubs connecting separated land areas, their detection in SAR images is of great significance. However, under complex scenarios, the sea surface conditions, the distribution of coastal terrain morphologies, and the scattering components of different structures in the bridge area are very complex and diverse, which makes the accurate and robust detection of sea-crossing bridges difficult, including the sea–land segmentation and bridge feature extraction on which the detection depends. In this paper, we propose a polarimetric SAR image detection method for sea-crossing bridges based on windowed level set segmentation and polarization parameter discrimination. Firstly, the sea and land are segmented by a proposed windowed level set segmentation method, which replaces the construction of the level set segmentation energy function based on the isolated pixel distribution with a joint distribution of pixels in a certain window region. Secondly, water regions of interest are extracted by a proposed water region merging algorithm combining the distances of the water contour and polarization similarity parameter. Finally, the bridge regions of interest (ROIs) are extracted by merging close water contours, and the ROIs are discriminated by the polarimetric parameters of the polarization entropy and scattering angle. Experimental results using multiple AirSAR, RADARSAT-2, and TerraSAR-X quad-polarization SAR data from the coastal areas of San Francisco in the USA, Singapore, and Fuzhou, Fujian, and Zhanjiang, Guangdong, in China show that the proposed method can achieve 100% detection of sea-crossing bridges in different bands for different scenes, and the accuracy of the intersection of the ground-truth (IoG) index of bridge body recognition can reach more than 85%. The proposed method can improve the detection rate and reduce the false alarm rate compared with the traditional spatial-based method.

Bridge-over-water detection plays vital role in urban surveillance and military reconnaissance. Bridges have arbitrary orientations and extreme aspect ratios in remote sensing images, and the preceding works cannot adequately extract bridge-related features. Small bridges are difficult to detect accurately in optical remote sensing images. The oriented bounding box annotations are required by previous deep-learning-based methods for detecting rotated objects. But obtaining the annotations is a laborious task. Though widely studied previously, they are still challenging problems. To address these problems, modulated deformable convolution and attention mechanisms were introduced in this paper. Modulated deformable convolution made the receptive field more flexible. The feature extraction capability of the network was enhanced. A new weighted structure was designed to quantify the contributions of channel and spatial attention mechanisms. A selective attention usage strategy was proposed to improve the detection performance. To locate bridge-over-water more precisely, a new bounding box conversion module was presented. There was no need for oriented bounding box annotations, and the process only relied on bridge-related prior knowledge. Multiple experiments were performed to verify the effectiveness of proposed methods.

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

Bridge dynamic load test signals are readily contaminated by environmental noise. This reduces the accuracy of bridge structure state assessment. To address this issue, this research proposes a denoising method that combines the hippopotamus optimization algorithm (HOA), variational mode decomposition (VMD), and singular spectrum analysis (SSA). The methodology follows three key phases: First, the HOA optimizes the critical parameters of VMD. Then, the optimized VMD decomposes raw signals into several intrinsic mode components (IMFs). The IMFs below the threshold are removed by calculating the correlation coefficient between each IMF and the original signal. Finally, SSA is introduced for secondary denoising, which helps reorganize bridge signals and eliminate local low-frequency oscillations. The simulation results show that compared with other methods, the root mean square error (RMSE), signal-to-noise ratio (SNR), mean square error (MSE), and mean absolute error (MAE) of the denoised signals achieve on average 16.22% reduction, 2.51% improvement, 62.02% diminution, and 43.74% decrease, respectively, across varying noise levels. Practical validation reveals superior performance metrics: a mean 12.81% lower normalization Shannon entropy ratio (NSER) and a mean 8.44% higher noise suppression ratio (NSR) compared to other techniques. This comprehensive approach effectively addresses noise components in bridge dynamic load test signals.

Two hybrid, entropy-guided node metrics are proposed for the k-hop environment: Entropy-Weighted Redundancy (EWR) and Normalized Entropy Density (NED). The central idea is to couple local Shannon entropy with neighborhood density/redundancy so that structural heterogeneity around a vertex is captured even when classical indices (e.g., degree or clustering) are similar. The metrics are formally defined and shown to be bounded, isomorphism-invariant, and stable under small edge edits. Their behavior is assessed on representative topologies (Erdős–Rényi, Barabási–Albert, Watts–Strogatz, random geometric graphs, and the Zephyr quantum architecture). Across these settings, EWR and NED display predominantly negative correlation with degree and provide information largely orthogonal to standard centralities; vertices with identical degree can differ by factors of two to three in the proposed scores, revealing bridges and heterogeneous regions. These properties indicate utility for vulnerability assessment, topology-aware optimization, and layout heuristics in engineered and quantum networks.

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

Background Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. Method A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. Results In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. Conclusions Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

Turnover frame is one of the core connection parts of the bridge detection vehicle. Its performances, including stiffness, manufacturability and durability, are essential for the bridge detection vehicle. Lightweight of turnover frame can decrease the total vehicle weight, which is well conformed to the Chinese national standard GB 1589–2016 (Limits of dimensions, axle load and masses for motor vehicles, trailers and combination vehicles). In this paper, the methods of topology and thickness optimization are utilized to design a new turnover frame. Firstly, the finite element (FE) models of original turnover frame and up mechanism are created, and then the von Mises stress and deformation are obtained. The FE results are validated by the stress experiment of the turnover frame, the maximum error is 7.8% relative to experiment results. Secondly, a novel layout of turnover frame is obtained using solid isotropic microstructure with penalization (SIMP) method under multi-conditions. The thickness optimization is carried out based on topology optimization results. The manufacturability and functionality are considered for optimized results. Finally, stress and durability experiment validations of optimal design are carried out. The stress results are consistent with the simulation, and the structure is in excellent conditions after 600,000 cycles of repeated loads under harsh working conditions in proving ground. By comparing the FE results between the original and optimal turnover frame, it shows that the optimal scheme has 21.5% reduction in total mass while maintaining better stiffness and durability.

Nowadays, bridges play a crucial role, especially with the significant increase in the number of vehicles being driven worldwide. Hence, it is crucial to safeguard these structures from damage. This study aims to achieve this objective by proposing a novel hybrid framework for automated delamination detection of bridge decks based on ground penetrating radar (GPR), a mature technique utilized to localize underground deterioration or damage of bridges. The proposed framework comprises synchrosqueezed wavelet transform (SSWT), convolutional neural network (CNN), transfer learning, and metaheuristic optimization. First, original 1-D GPR signals undergo processing by SSWT to extract time–frequency characteristics that are sensitive to delamination. Next, extracted features are fed into deep CNN model VGG16 to develop a predictive model based on transfer learning. To enhance the generalization capability of the proposed model, modified whale optimization algorithm (MWOA) is utilized to optimize network hyperparameters during the training process. The performance of the proposed hybrid framework for delamination identification is validated using test data collected from the field testing of real bridges using GPR device. The proposed method demonstrates satisfactory results compared to other commonly used techniques, with the prediction accuracy of over 94%, providing an effective and efficient solution to the challenges of bridge defect detection.