csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

Xu, Zhenhua; Liu, Yuxuan; Gan, Lu; Hu, Xiangcheng; Sun, Yuxiang; Liu, Ming; Wang, Lujia

doi:10.48550/arxiv.2111.06020

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Due to the shape characteristics of the road network, there are some works on the detection task of objects that are similar to the road network, such as road boundaries [17]- [19], road lane lines [20]- [22], road lane [23], [24] and road curbs [25], [26]. Even though these works do not work on road network detection problems, their task is similar to ours and some ideas or techniques are inspiring to us.…”

Section: Graph Detection Of Objects Similar To Road Networkmentioning

confidence: 99%

RNGDet: Road Network Graph Detection by Transformer in Aerial Images

Xu,

Liu,

Gan

et al. 2022

Preprint

Self Cite

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Road network graphs provide critical information for autonomous vehicle applications, such as motion planning on drivable areas. However, manually annotating road network graphs is inefficient and labor-intensive. Automatically detecting road network graphs could alleviate this issue, but existing works are either segmentation-based approaches that could not ensure satisfactory topology correctness, or graph-based approaches that could not present precise enough detection results. To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning named RNGDet (Road Network Graph Detection by Transformer) in this paper. In view of that high-resolution aerial images could be easily accessed all over the world nowadays, we make use of aerial images in our approach. Taken as input an aerial image, our approach iteratively generates road network graphs vertex-by-vertex. Our approach can handle complicated intersection points of various numbers of road segments. We evaluate our approach on a publicly available dataset. The superiority of our approach is demonstrated through the comparative experiments.

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Section: Graph Detection Of Objects Similar To Road Networkmentioning

confidence: 99%

RNGDet: Road Network Graph Detection by Transformer in Aerial Images

Xu,

Liu,

Gan

et al. 2022

Preprint

Self Cite

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“…Their recognition effect and accuracy requirements are highly guaranteed. In terms of image superresolution, XuChen Zhen, Hua Xu and LeFei Zhang [23][24][25] used GANs to conduct super-resolution research on remote sensing images, which led to high accuracy in identifying street images. GANs have more vital feature learning and generation abilities than ordinary network models.…”

Section: Introductionmentioning

confidence: 99%

Improved CycleGAN for super-resolution of engineering drawings

Song

Yao²,

Tian³

et al. 2023

Meas. Sci. Technol.

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In intelligent manufacturing, the quality of machine translation engineering drawings will directly affect its manufacturing accuracy. Currently, most of the engineering drawing translation work is done manually, which greatly reduces production efficiency. This paper proposes an automatic translation method for welded structural engineering drawings based on Cyclic Generative Adversarial Networks (Cyclegan). The Cyclegan network model of unpaired transfer learning is used to learn the feature mapping of real welding engineering drawings to realize the automatic translation of engineering drawings. U-Net and Patchgan are the main network for the generator and discriminator, respectively. Based on removing the identity mapping function, a high-dimensional sparse network is proposed to replace the traditional dense network for the Cyclegan generator to improve noise robustness. Increase the residual block hidden layer to increase the resolution of the generated graph. The improved and fine-tuned network models are experimentally validated, computing the gap between real and generated data. It meets the welding engineering precision standard and solves the main problem of low drawing recognition efficiency in the welding manufacturing process. The results show. After training with our model, the PSNR, SSIM, and MSE of welding engineering drawings reach about 44.89%, 99.58%, and 2.11, respectively, which are superior to traditional networks in both training speed and accuracy.

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csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

Cited by 2 publications

References 20 publications

RNGDet: Road Network Graph Detection by Transformer in Aerial Images

RNGDet: Road Network Graph Detection by Transformer in Aerial Images

Improved CycleGAN for super-resolution of engineering drawings

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