PharmaNet Deep: Real-Time Pharmaceutical Defect Detection Using Defect-Guided Feature Fusion and Uncertainty-Driven Inspection

Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy, potentially compromising treatment outcomes. Consequently, detecting tablet defects post-blister packaging in real-time represents a critical challenge in the pharmaceutical industry. Additionally, factors such as blister reflections and limited dataset size hinder the deep learning model's ability to identify defects accurately. To address these challenges, the PharmaNet deep model is developed utilizing a convolutional neural network (CNN) architecture, incorporating defect-guided dynamic feature fusion (DGDFF) in which the fusion process is dynamically guided by potential defect regions, allowing the model to focus on relevant features (defect areas) more efficiently, adaptive deep chain (ADC) which includes occlusion pattern generator (OPG) and residual recursive feature reconstructor (R2FR). The OPG creates multiple views of potential defect regions by systematically dividing features into blocks and creating layered occlusions. At the same time, the R2FR uses gates with ELU activation and residual connections to reconstruct detailed features from these occluded sequences, ultimately enhancing the model's ability to detect subtle defects. The model culminates in an uncertainty-aware detection head that enhances defect prediction reliability by incorporating uncertainty estimates alongside traditional class probabilities and bounding box predictions. This provides a more informed and interpretable decision-making process for pharmaceutical quality control in real-time. Empirical evaluation on the proposed model demonstrates state-of-the-art performance with 99.4% mAP on the PharmaBlister dataset and 97.2% mAP on MVTech AD, with minimal predictive uncertainty, validating its efficacy in pharmaceutical quality control applications.