CENet: improve counting performance of X-ray surface mounted chip counter via scale favor and cell extraction

Yuanzhao Shao () and Yonghong Song ()
Additional contact information
Yuanzhao Shao: Xi’an Jiaotong University
Yonghong Song: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 16, 303-317

Abstract: Abstract The X-ray Surface Mounted Chip Counter (X-SMDCC) relies on a counting algorithm to count the number of surface-mounted chips, enabling convenient and fast counting. It is an efficient auxiliary equipment for SMT material management. However, most existing counting algorithms use crowd counting algorithms for fine-tuning, without designing a special structure to optimize processing based on the differences and characteristics of data in crowd counting and X-SMDCC, leading to inaccurate counting results under the condition of chip scale change or adhesion. In this work, we propose a cell extraction network to address the issues of scale difference and adhesion, which improves the counting accuracy of X-SMDCC. Firstly, we present a scale-favoring module to handle scale differences between different images, as we notice that the scale difference only appears between different images. Furthermore, we propose a cell extraction module to process adhesive regions since we discovered that the human eye can process adhesive regions through comparison while labeling data. Additionally, we recommend using a shape-constrained inverse distance transform map as a learning target. We conducted numerous experiments on the SMD-Chip-179 dataset and found that our method is significantly superior to current advanced counting methods.

Keywords: Counting; X-ray Surface Mounted Chip counter; Multi-scale feature fusion; Cell extraction (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02223-z 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:1:d:10.1007_s10845-023-02223-z

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

DOI: 10.1007/s10845-023-02223-z

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 ().