Detecting interchanges in road networks using a graph convolutional network approach

Yang, Min; Jiang, Chenjun; Yan, Xiongfeng; Ai, Tinghua; Cao, Minjun; Chen, Wenyuan

doi:10.1080/13658816.2021.2024195

Cited by 21 publications

(5 citation statements)

References 28 publications

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“…(2) Both the temporal and spatial graph structures are predefined, and although some research [9] has used learnable adjacency matrices, they are still limited by the conventional graph convolutional architecture.…”

Section: Graph Convolutional Neural Network Structurementioning

confidence: 99%

See 1 more Smart Citation

Fast pulse detection algorithm based on improved ghost convolution

Qiu,

Dai,

Chen

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Fast pulse detection in pulse images has received widespread attention in the field of underwater computer vision, and deep learning-based fast pulse detection algorithms can clearly represent the characteristics of sonar pulses, thus gradually becoming an important research direction in this field. To address the issues of large network parameters and high computational complexity in mainstream sonar pulse detection and recognition algorithms, a lightweight convolutional network that incorporates ghost convolution is designed and applied to fast pulse detection in sonar. The ghost convolution module is designed and placed in different areas to further reduce the network parameters from the network structure. The experiment is conducted on the constructed pulse dataset, and the results show that compared with the traditional model, the sonar pulse fast detection algorithm based on this lightweight convolutional neural network can achieve higher recognition accuracy while maintaining a lower network parameter amount.

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Section: Graph Convolutional Neural Network Structurementioning

confidence: 99%

“…The Ghost module proposed by HAN et al [9] can effectively solve the above-mentioned problem. A trained deep neural network typically contains many redundant feature maps, and some of these feature maps are similar to each other.…”

Section: Ghost Modulementioning

confidence: 99%

Fast pulse detection algorithm based on improved ghost convolution

Qiu,

Dai,

Chen

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

View full text Add to dashboard Cite

show abstract

“…Road generalization is an important research direction in the field of map generalization [1][2][3]. It mainly includes road selection [4,5], road simplification [6,7], road pattern recognition [8], and road displacement [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Methods of Resolving Road Graphic Conflicts Based on Cartographic Rules and Generalization Operations

Zheng,

Guo,

Wang

et al. 2024

IJGI

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The resolution of road graphic conflicts is a key aspect of map generalization, which involves both scale reduction and the symbolization of map features. This study proposes collaborative methods of road graphic conflict resolution considering different road characteristics. These methods consider both geometric and semantic characteristics, and they incorporate the bend characteristics of roads, the road symbol size, and road semantics. Constrained Delaunay triangulation skeleton lines are used to categorize road graphic conflicts, which are made up of four independent conflict types and four group conflict types. Based on their characteristics, three collaborative methods are designed to deal with the different types of road graphic conflicts: collaboration between deletion and the snake displacement model, collaboration between the snake displacement model and collinearity, and collaboration among simplification, smoothing, and the beam displacement model. Two types of independent conflicts can be processed using only one simple operation. This study summarizes the cartographic rules for resolving road graphic conflicts, and these are used along with geometric features to drive the collaborative methods or one simple operation presented here. The experimental results indicate that the method proposed in this study can effectively resolve road graphic conflicts.

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“…Simple junctions, such as planar crossroads and T-shaped road intersections, are defined as intersections where roads directly meet, whereas complex junctions connect two or more primary roads though slip roads or ramps. Complex junctions involve two different scenarios: planar structures with slip roads for smooth turning and grade-separated interchanges with ramps that allow vehicles to travel from one road to another without interruptions [9][10][11]. In general, complex junctions are places where roads meet in a complex manner.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [9] utilized a target detection model, that is, the faster-region convolutional neural network, to detect the locations of interchanges based on raster representations of road networks. Yang et al [10] developed a graph-based, deep-learning approach to detect segments belonging to interchanges in road networks. Interchange structures were obtained by clustering the detected interchange segments.…”

Section: Introductionmentioning

confidence: 99%

A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

Yang

Cheng

Cao

et al. 2022

IJGI

Self Cite

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Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.

show abstract

Detecting interchanges in road networks using a graph convolutional network approach

Cited by 21 publications

References 28 publications

Fast pulse detection algorithm based on improved ghost convolution

Fast pulse detection algorithm based on improved ghost convolution

Collaborative Methods of Resolving Road Graphic Conflicts Based on Cartographic Rules and Generalization Operations

A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

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