Intravenous fluid bags are essential in hospitals, but foreign particles can contaminate them during mass production, posing significant risks. Although produced in sanitary environments, contamination can cause sever...
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Intravenous fluid bags are essential in hospitals, but foreign particles can contaminate them during mass production, posing significant risks. Although produced in sanitary environments, contamination can cause severe problems if products reach consumers. Traditional inspection methods struggle with the flexible nature of these bags, which deform easily, complicating particle detection. Recent deep learning advancements offer promising solutions in regard to quality inspection, but high-resolution image processing remains challenging. This paper introduces a real-time deep learning-based inspection system addressing bag deformation and memory constraints for high-resolution images. The system uses object-level background rejection, filtering out objects similar to the background to isolate moving foreign particles. To further enhance performance, the method aggregates objectpatches, reducing unnecessary data and preserving spatial resolution for accurate detection. During aggregation, candidate objects are tracked across frames, forming tracks re-identified as bubbles or particles by the deep learning model. Ensemble detection results provide robust final decisions. Experiments demonstrate that this system effectively detects particles in real-time with over 98% accuracy, leveraging deep learning advancements to tackle the complexities of inspecting flexible fluid bags.
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