Point Symbol Recognition Algorithm based on Improved Generalized Hough Transform and Nonlinear Mapping

Song, Jianfeng; Zhang, Zhen; Qi, Yutao; Miao, Qiguang

doi:10.1109/spac46244.2018.8965548

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Since the accuracy of statistical methods is significantly impacted by the way of creating features, many workers studied various feature engineering methods, such as kernel density (W. Zhang et al., 2006), sparse representation (Do et al., 2016), and improved generalized Hough transform (J. Song et al., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

An automation solution to convert CAD engineering drawings into railroad station models

Wang,

Li,

Zhang

2023

Computer aided Civil Eng

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Creating a high‐fidelity railroad station model to match the physical details of hundreds of tracks and switches is never a trivial task. The manual modeling approach often costs engineers significant efforts and constrains the generality and extensivity of many advanced methods. Taking advantage that many stations are drawn proportionally into two‐dimensional drawing exchange format (DXF) files via state‐of‐the‐art computer‐aided design (CAD) techniques, this paper proposed an efficient solution to convert DXF files into meaningful station models. The proposed solution consists of two phases (1) converting graphic basic primitives without explicit engineering interpretations into recognizable railroad symbols and (2) modeling undirected railroad station graphs with necessary configurations such as endpoints and routes. The proposed solution is developed into a graphic user interface application with minimal user interactions and subsequently tested at several real‐world passenger stations in Asia for its validity, productivity, and applicability.

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Section: Literature Reviewmentioning

confidence: 99%

An automation solution to convert CAD engineering drawings into railroad station models

Wang,

Li,

Zhang

2023

Computer aided Civil Eng

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“…With the development of object detection methods from traditional computer-vision-based approaches [25][26][27][28][29] to deep learning-based object detection models [30][31][32][33][34][35], object detection methods have attracted increasing attention from researchers in the field of image recognition due to their high accuracy and fast recognition speed. Song et al [36] proposed an improved method for map point symbol recognition based on improved generalized Hough transform and non-line AR mapping, which achieved high accuracy in symbol category recognition and positioning. Huang et al [37] used an improved YOLOv4 object detection model to recognize point symbols in scanned topographic maps, which has higher accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Zou,

Xu,

et al. 2023

IJGI

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Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for AR maps heavily relies on manual prefabrication. It leads to repetitive and error-prone prefabrication work, which restricts the design of the dynamic, interactive functions of AR maps. To solve this problem, this paper explored the possibility of automatically generating AR map prefabs using object detection models to establish a data conversion interface from paper maps to AR maps. First, we compared and analyzed various object detection models and selected YOLOv8x to recognize map point symbols. Then, we proposed a method to automatically generate AR map prefabs based on the predicted bounding boxes of the object detection model, which could generate prefabs with corresponding categories and positional information. Finally, we developed an AR map prototype system based on Android mobile devices. We designed an interaction method for information queries in the system to verify the effectiveness of the method proposed in this paper. The validation results indicate that our method can be practically applied to the AR map prefabrication process and can quickly generate AR map prefabs with high information accuracy. It alleviated the repetitive workload established through the manual prefabrication method and had specific feasibility and practicality. Moreover, it could provide solid data support for developing dynamic interactive functions of AR maps.

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Leveraging Deep Convolutional Neural Network for Point Symbol Recognition in Scanned Topographic Maps

Huang

Qun

et al. 2023

IJGI

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Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.

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Point Symbol Recognition Algorithm based on Improved Generalized Hough Transform and Nonlinear Mapping

Cited by 3 publications

References 9 publications

An automation solution to convert CAD engineering drawings into railroad station models

An automation solution to convert CAD engineering drawings into railroad station models

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Leveraging Deep Convolutional Neural Network for Point Symbol Recognition in Scanned Topographic Maps

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