The accurate extraction of traffic signs is of great significance to the digitization of traffic information and the fine management of traffic. This article introduces an innovative approach to address the challenges associated with recognizing and detecting traffic signs, considering their vulnerability to complex backgrounds, variations in illumination, and motion blur. The proposed method utilizes a semi-supervised learning (SSL) strategy, combining convolutional neural networks (CNNs) with a transformer encoder–decoder architecture, to extract traffic sign features from vehicle panoramic images. To enhance feature extraction, a hierarchical sampling method (HSM) is introduced, which facilitates the extraction of multiscale self-attention features in the transformer encoder–decoder structure. Additionally, a network module called local and global information aggregator (LGIA) is designed based on HSM, enabling the incorporation of both local and global context information. Furthermore, a SSL strategy is adopted to simultaneously train our model using both labeled and unlabeled data samples. This strategy aims to improve the extraction of traffic signs by capitalizing on the broader data set available through unlabeled data. Experimental results demonstrate the effectiveness and robustness of the proposed method in improving the detection and recognition of traffic signs. The approach showcases significant improvements in overcoming the challenges posed by complex backgrounds, variations in illumination, and motion blur. Our approach achieved a 0.9% improvement in the F1-score evaluation over the current classical object detection algorithm on the public data set Tsinghua-Tencent 100K and a 1.1% improvement on the SSW data set.

Machine learning techniques have become increasingly applied to non-destructive testing based on pulsed thermography. However, existing methods need to extract characteristic data manually. The present work addresses this issue by applying a bidirectional long-short term memory (Bi-LSTM) network to identify defects, predict defect depths, and reconstruct defective materials in three dimensions automatically based on raw cooling data sequences. The network is trained and tested based on data collected for stainless-steel specimens with multiple flat-bottom holes introduced at various specimen depths. A dual-task method and a single-task method were proposed based on Bi-LSTM network. A classification model and a regression model are constructed in the dual-task method for identifying defects and predicting defect depths. Only the regression network is implemented in a single-task method to more quickly obtain the same results based on a depth threshold. Both methods are demonstrated to achieve satisfactory accuracy in the 3D reconstruction of the defects in the testing specimen. In addition, higher pulse energy and faster acquisition frequency can promote the prediction accuracy. Then the results of Bi-LSTM were compared with the results of 1D CNN and MLP. To verify the generalization of the proposed method, CFRP specimen is employed for 3D reconstruction, which also performed with good results.

Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information of road surface element, we propose a content-adaptive hierarchical deep learning model to detect arbitrary-oriented road surface elements from mobile laser scanning (MLS) point clouds. In the model, we design a densely connected feature integration module (DCFM) to connect and reorganize feature maps of each stage in the backbone network. Besides, we propose a hierarchical prediction module (HPM) to innovatively use the reorganized feature maps to recognize different types of road surface elements, and thus, semantic information of road surface element can be adaptively expressed on multilevel feature maps. We also add a cascade structure (CS) in the head of model to detect the target efficiently, which can learn the offset between the predicted minimum bounding box of road surface element and ground truth. In experiments, we prove that the proposed method mainly contributed by HPM can maintain robust detection performance, even in the cases of unbalanced category number or overlapping of road surface elements. The experiments also prove that the proposed DCFM can improve the recognition effects of small targets. The CS for predicting boundary offset can detect each target more accurately. We also integrate the designed modules into some rotation detectors, e.g., the EAST and R3Det, and achieve the state-of-the-art results in three road scenes with different categories and uneven distribution of road surface elements, which further shows the effectiveness of the proposed method.

The maintenance of subway tunnels is vital to ensure the safety of their daily operation. Issues experienced by shield subway tunnels, especially the water leakages, require rapid and accurate detection and diagnosis. Due to the large number of disturbances in the tunnels, conventional algorithms face limitations when extracting discriminative features. To solve this problem, we propose a novel and efficient deep learning model for extracting multiscale and discriminative features of water leakages based on mobile laser scanning (MLS) point cloud intensity images. A new residual network module (Res2Net) is integrated with a cascade structure to form a unified model to extract the multiscale features of water leakages. The model can fully consider geometric characteristics of water leakages and grade the residual connections in a single residual block. This expands the size of receptive field in each network layer and can better facilitate the extraction of geometric characteristics of water leakages. Finally, we verify the advantages of the proposed method via experiments on five water leakage datasets of tunnel intensity images converted from point clouds obtained by a self-developed MLS system and compare its performance with other methods.

MOTIVATION: Native mass spectrometry is now a well-established method for the investigation of protein complexes, specifically their subunit stoichiometry and ligand binding properties. Recent advances allowing the analysis of complex mixtures lead to an increasing diversity and complexity in the spectra obtained. These spectra can be time-consuming to tackle through manual assignment and challenging for automated approaches. RESULTS: Native Mass Spectrometry Visual Analyser is a web-based tool to augment the manual process of peak assignment. In addition to matching masses to the stoichiometry of its component subunits, it allows raw data processing, assignment and annotation and permits mass spectra to be shared with their respective interpretation. AVAILABILITY AND IMPLEMENTATION: NaViA is open-source and can be accessed online under https://navia.ms. The source code and documentation can be accessed at https://github.com/d-que/navia, under the BSD 2-Clause licence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the shallow feature maps with high resolution and abundant image details can utilize the deep features. Thus, the DFPN can learn hierarchical deep detailed features. The designed deep learning model was trained end to end for road marking instance extraction with mobile laser scanning (MLS) point clouds. Then, we introduced the focal loss function into the optimization of deep learning model in road marking segmentation part, to pay more attention to the hard-classified samples with a large extent of background. In the experiments, our method can achieve better results than state-of-the-art methods on instance segmentation of road markings, which illustrated the advantage of the proposed method.

Classification of airborne laser scanning (ALS) point clouds is needed in digital cities and 3-D modeling. To efficiently recognize objects in ALS point clouds, we propose a novel hierarchical aggregated deep feature representation method, which can adequately employ spatial association of multilevel structures and deep feature discrimination. In our method, a 3-D deep learning model is constructed to represent the discriminative feature of each point cluster in a hierarchical structure by decreasing the within-class distance and increasing the between-class distance. Our method aggregates the discriminative deep features in different levels into a hierarchical aggregated deep feature that considers the spatial hierarchy and feature distinctiveness. Lastly, we build a multichannel 1-D convolutional neural network to classify the unknown points. Our tests demonstrate that the proposed hierarchical aggregated deep feature method can enhance point cloud classification results. Comparing with seven state-of-the-art methods, those results also verified the superior performance of our method.