Multi-Level Scale Attention Fusion Network for Adhesive Spots Segmentation in Microlens Packaging.

Yixiong Yan, Sijia Chen, Lian Duan, Dinghui Luo, Fan Zhang, Shunshun Zhong

Abstract read

In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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5 · Who and what money

Authors and funding

6 authors.

Yixiong YanCollege of Mechanical and Vehicle Engineering, Changsha University of Science & Technology, Changsha 410114, China.ORCID 0000-0002-4887-3877

Sijia ChenCollege of Mechanical and Vehicle Engineering, Changsha University of Science & Technology, Changsha 410114, China.

Lian DuanState Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.ORCID 0000-0001-5370-2565

Dinghui LuoState Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

Fan ZhangState Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

Shunshun ZhongState Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

Funding

National Natural Science Foundation of China Grant no.52405609

6 · The paper itself

Abstract

The demand for high-quality beams from high-power lasers has led to the need for high-precision inspection of adhesion points for collimating lens packages. In this paper, we propose a Multi-Level Scale Attention Fusion Network (MLSAFNet) by fusing a Multi-Level Attention Module (MLAM) and a Multi-Scale Channel-Guided Module (MSCGM) to achieve highly accurate and robust adhesive spots detection. Additionally, we built a Laser Lens Adhesive Spots (LLAS) dataset using automated lens packaging equipment and performed pixel-by-pixel standardization for the first time. Extensive experimental results show that the mean intersection over union (mIoU) of MLSAFNet reaches 91.15%, and its maximum values of localization error and area measurement error are 21.83 μm and 0.003 mm

Indexed as

adhesive spots segmentationmulti-level attentionmulti-scale channel-guided

Identifiers

PMID41011931

DOI10.3390/mi16091043

PMCPMC12471493

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.