Research on salient object detection algorithm for complex electrical components

Jinyu Tian (), Zhiqiang Zeng (), Zhiyong Hong () and Dexin Zhen ()
Additional contact information
Jinyu Tian: Wuyi University
Zhiqiang Zeng: Wuyi University
Zhiyong Hong: Wuyi University
Dexin Zhen: Wuyi University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 6, No 16, 4005-4023

Abstract: Abstract Due to the complexity of electrical components, traditional edge detection methods cannot always accurately extract key edge features of them. Therefore, this study constructs a dataset of complex electrical components and proposes a Step-by-Level Multi-Scale Extraction, Fusion, and Refinement Network (SMFRNet) that is based on the salient object detection algorithm. As detailed features includes a wealth of texture and shape characteristics that are related to edges, so the Hierarchical Deep Aggregation U-block (HDAU) is incorporated in the encoder as a means of capturing more details through hierarchical aggregation. Meanwhile, the proposed Multi-Scale Pyramid Convolutional Fusion (MPCF) and Fusion Attention Structure (FAS) achieve step-by-level feature refinement to obtain finer edges. In order to address the issues of imbalanced pixel categories and the difficulty in separating edge pixels, a hybrid loss function is also constructed. The experimental results indicate that this method outperforms nine state-of-the-art algorithms, enabling the extraction of high-precision key edge features. It provides a reliable method for key edge extraction in complex electrical components and provides important technical support for automated components measurement.

Keywords: Complex electrical components; Key edge feature extraction; Salient object detection; Boundary refinement (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02434-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02434-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02434-y

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().