Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13081333

Authors: Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

Institution: Beijing Jiaotong University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137