Automated Tablet Defect Inspection

The text discusses a two-stage computer vision pipeline for automating pharmaceutical quality control using Roboflow's RF-DETR and a vision language model (VLM) to detect and classify tablet defects, addressing common manufacturing issues such as capping, lamination, chipping, and cracking. The pipeline involves training an RF-DETR model to locate tablets, followed by the VLM classifying each tablet's defect type, which is then processed through a Custom Python Block to provide a pass or fail verdict accompanied by a structured report. This system allows for independent evolution of the detection and classification stages, as the detector remains constant while the VLM prompt can be updated to recognize new defect categories without retraining. The pipeline's adaptability is highlighted, as it can be applied to various products by changing the detection dataset and updating the VLM prompt, thus facilitating efficient inspection processes without frequent retraining cycles.

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