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Pose-guided YOLO-CED: A two-stage framework for robust SMD-PCB defect detection in mobile manipulators.

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Τίτλος: Pose-guided YOLO-CED: A two-stage framework for robust SMD-PCB defect detection in mobile manipulators.
Συγγραφείς: Sun M; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: sunmingxiao@hrbust.edu.cn., Zhao Q; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: z_qiang2000@163.com., Zhang Q; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: zhangqiuyu@hrbust.edu.cn., Luan T; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: luantiantian@hrbust.edu.cn.
Πηγή: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Nov; Vol. 203, pp. 109136. Date of Electronic Publication: 2026 May 17.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s): Original Publication: New York : Pergamon Press, [c1988-
Ιατρικοί όροι (MeSH): Detection Algorithms*
Περίληψη: The stability of printed circuit boards (PCB) and their surface-mount devices (SMD) holds significant importance in industrial production applications. However, when deploying mobile manipulator to replace manual safety inspections in manufacturing environments, challenges persist due to uncertainties in PCB spatial pose, significant variations in SMD dimensions, and computational limitations. Achieving stable identification across all defect categories remains difficult. To address this, this paper proposes a two-stage method for SMD-PCB defect detection in mobile manipulator, combining target pose estimation with YOLO-CED. First, a color attention mechanism is introduced. By associating color features with spatial features, it adaptively assigns appropriate weights to different regions. High-precision planar detection enhances the robustness of point cloud-based pose estimation. Second, an adaptive gating mechanism with learnable parameters is introduced for feature extraction, handling channel weights for three-branch parallel feature acquisition fusion and lightweight spatial weights. Proposing a multi-sensory field feature acquisition mechanism and an edge sharpening feature fusion mechanism based on Laplace operator convolution. Concurrently, a detail restoration branch ensures precise boundary localization by preserving fine features of small objects. Finally, the spatial reshaping branch features are processed through an L2-normalized self-supervised multi-head attention mechanism, and the dual-branch features are fused to produce the final output. Experimental results demonstrate that the proposed method outperforms multiple lightweight models on the self-built HUSTPCB dataset (covering 10 common defect categories), achieving a mean average precision (mAP) of 81.7%. This algorithm was verified on a mobile platform integrated with a robotic arm, demonstrating the application prospects of the mobile manipulator in flexible PCB manufacturing. Meanwhile, its excellent performance on the PKU-Market-PCB dataset proved the universality and transferability of the YOLO-CED.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Defect detection; Mobile manipulator; Point cloud model; Printed circuit boards(PCB); YOLO11
Entry Date(s): Date Created: 20260521 Date Completed: 20260811 Latest Revision: 20260812
Update Code: 20260813
DOI: 10.1016/j.neunet.2026.109136
PMID: 42166966
Βάση Δεδομένων: MEDLINE
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  Data: Pose-guided YOLO-CED: A two-stage framework for robust SMD-PCB defect detection in mobile manipulators.
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  Data: <searchLink fieldCode="AU" term="%22Sun+M%22">Sun M</searchLink>; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: sunmingxiao@hrbust.edu.cn.<br /><searchLink fieldCode="AU" term="%22Zhao+Q%22">Zhao Q</searchLink>; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: z_qiang2000@163.com.<br /><searchLink fieldCode="AU" term="%22Zhang+Q%22">Zhang Q</searchLink>; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: zhangqiuyu@hrbust.edu.cn.<br /><searchLink fieldCode="AU" term="%22Luan+T%22">Luan T</searchLink>; Harbin University of Science and Technology, No 52, Xuefu Road, Nangang District Harbin, 150080, China. Electronic address: luantiantian@hrbust.edu.cn.
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  Data: <searchLink fieldCode="JN" term="%228805018%22">Neural networks : the official journal of the International Neural Network Society</searchLink> [Neural Netw] 2026 Nov; Vol. 203, pp. 109136. <i>Date of Electronic Publication: </i>2026 May 17.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Pergamon+Press%22">Pergamon Press </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>8805018 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-2782 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2208936080%22">08936080 </searchLink><i>NLM ISO Abbreviation: </i>Neural Netw <i>Subsets: </i>MEDLINE
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  Data: The stability of printed circuit boards (PCB) and their surface-mount devices (SMD) holds significant importance in industrial production applications. However, when deploying mobile manipulator to replace manual safety inspections in manufacturing environments, challenges persist due to uncertainties in PCB spatial pose, significant variations in SMD dimensions, and computational limitations. Achieving stable identification across all defect categories remains difficult. To address this, this paper proposes a two-stage method for SMD-PCB defect detection in mobile manipulator, combining target pose estimation with YOLO-CED. First, a color attention mechanism is introduced. By associating color features with spatial features, it adaptively assigns appropriate weights to different regions. High-precision planar detection enhances the robustness of point cloud-based pose estimation. Second, an adaptive gating mechanism with learnable parameters is introduced for feature extraction, handling channel weights for three-branch parallel feature acquisition fusion and lightweight spatial weights. Proposing a multi-sensory field feature acquisition mechanism and an edge sharpening feature fusion mechanism based on Laplace operator convolution. Concurrently, a detail restoration branch ensures precise boundary localization by preserving fine features of small objects. Finally, the spatial reshaping branch features are processed through an L2-normalized self-supervised multi-head attention mechanism, and the dual-branch features are fused to produce the final output. Experimental results demonstrate that the proposed method outperforms multiple lightweight models on the self-built HUSTPCB dataset (covering 10 common defect categories), achieving a mean average precision (mAP) of 81.7%. This algorithm was verified on a mobile platform integrated with a robotic arm, demonstrating the application prospects of the mobile manipulator in flexible PCB manufacturing. Meanwhile, its excellent performance on the PKU-Market-PCB dataset proved the universality and transferability of the YOLO-CED.<br /> (Copyright © 2026 Elsevier Ltd. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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      – TitleFull: Pose-guided YOLO-CED: A two-stage framework for robust SMD-PCB defect detection in mobile manipulators.
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