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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3485-3489

Lightweight edge detection neural network with thinning edges

Pu Yingkai
Zhang Duzhen
School of Computer Science & Technology, Jiangsu Normal University, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0122

Abstract

Aiming at the problems of high complexity, large amount of parameters, low accuracy and efficiency of edge detection models, this paper proposed a light-weight edge detection neural network. First of all, this paper used the MP module to divide the original image into small blocks of the same dimension and perform position coding to enhance the connection between edge pixels. Then, it extracted and fused the features of different regions of the image through multiple convolution operations in MGC to reduce redundant information. Finally, this paper adjusted the resolution size of the output image by performing multiple deconvolution up-sampling operations, in order to output the predicted edge image. The final network has only 125 KB parameters. Experiments were carried out on BIPED and MDBD datasets to verify the comprehensive ability of the model to detect edges. Compared with the most advanced lightweight edge detection method LDC, the test results on BIPED dataset show that the index ODS is only reduced by 0.9%, the number of model parameters is reduced by 81.5%, and the FPS is increased by 89.0%. It can meet the needs of practical tasks while maintaining the fine-grained edge recognition.

Foundation Support

江苏省高等学校自然科学研究面上项目(19KJB520032)
江苏师范大学博士学位教师科研支持项目(20XSRS018)
江苏师范大学科研与实践创新项目(2022XKT1539)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0122
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Technology of Graphic & Image
Pages: 3485-3489
Serial Number: 1001-3695(2023)11-044-3485-05

Publish History

[2023-06-01] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

浦莹开, 张笃振. 一种细化边缘的轻量级边缘检测神经网络 [J]. 计算机应用研究, 2023, 40 (11): 3485-3489. (Pu Yingkai, Zhang Duzhen. Lightweight edge detection neural network with thinning edges [J]. Application Research of Computers, 2023, 40 (11): 3485-3489. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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