M-DSFBNet: a multi-scale dual-stream feature fusion network for surface defect detection in blister pharmaceutical

Abstract Precise defect detection of aluminum-plastic blister packaging before drug boxing has become a critical step in pharmaceutical production. To address the common challenges in current algorithms, such as the difficulty in identifying small and complex defect features and low detection accuracy, while ensuring real-time performance and efficiency, we propose a computer vision-based multi-scale dual-stream feature fusion neural network, named M-DSFBNet. First, we introduce the multi-scale frequency-aware convolutional module, which enhances feature representation capabilities through a split-perception-selection strategy to improve network performance, while enabling lightweight network processing. Additionally, we design a neck fusion network that improves the aggregation and fusion of multi-scale feature information via the MSTB (multi-scale feature transformation block) with dual-path inputs. We collected defect samples and constructed the ABP-DET dataset for aluminum-plastic blister drug packaging surface defects, aimed at supporting the training and evaluation of detection models. Experimental results on the ABP-DET dataset show that M-DSFBNet achieves an average precision (mAP50) of 94.1%, while maintaining low computational complexity and parameter count, demonstrating a good balance between detection accuracy and computational efficiency.In addition, our model achieved an mAP50 of 79.9% on the NEU-DET dataset, demonstrating the strong generalization capability of M-DSFBNet.