Multi-Frame Joint Detection Approach for Foreign Object Detection in Large-Volume Parenterals

Ziqi Li, Dongyao Jia (), Zihao He and Nengkai Wu
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Ziqi Li: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Dongyao Jia: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Zihao He: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Nengkai Wu: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2025, vol. 13, issue 8, 1-22

Abstract: Large-volume parenterals (LVPs), as essential medical products, are widely used in healthcare settings, making their safety inspection crucial. Current methods for detecting foreign particles in LVP solutions through image analysis primarily rely on single-frame detection or simple temporal smoothing strategies, which fail to effectively utilize spatiotemporal correlations across multiple frames. Factors such as occlusion, motion blur, and refractive distortion can significantly impact detection accuracy. To address these challenges, this paper proposes a multi-frame object detection framework based on spatiotemporal collaborative learning, incorporating three key innovations: a YOLO network optimized with deformable convolution, a differentiable cross-frame association module, and an uncertainty-aware feature fusion and re-identification module. Experimental results demonstrate that our method achieves a 97% detection rate for contaminated LVP solutions on the LVPD dataset. Furthermore, the proposed method enables end-to-end training and processes five bottles per second, meeting the requirements for real-time pipeline applications.

Keywords: object detection; multi-object track; cross-frame feature association; large-volume parenterals; re-identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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