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LDC: Lightweight Dense CNN for Edge Detection

IEEE access, 2022, Vol.10, p.1-1 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2022.3186344 ;CODEN: IAECCG

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  • Title:
    LDC: Lightweight Dense CNN for Edge Detection
  • Author: Soria, Xavier ; Pomboza-Junez, Gonzalo ; Sappa, Angel
  • Subjects: Annotations ; boundary detection ; Computer architecture ; Deep learning ; Detectors ; Edge detection ; Image edge detection ; Kernel ; Lightweight ; Mathematical models ; Parameters ; Source code ; Training
  • Is Part Of: IEEE access, 2022, Vol.10, p.1-1
  • Description: This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 2% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code will be available.
  • Publisher: Piscataway: IEEE
  • Language: English
  • Identifier: ISSN: 2169-3536
    EISSN: 2169-3536
    DOI: 10.1109/ACCESS.2022.3186344
    CODEN: IAECCG
  • Source: IEEE Open Access Journals
    DOAJ Directory of Open Access Journals
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