Detection of Fiber-Flaw on Pill Surface Based on Lightweight Network SA-MGhost-DVGG

Fiber-flaw detection on pill surfaces is a critical yet challenging task in industrial pharmacy due to diverse defect characteristics. To overcome the limitations of traditional methods in accuracy and real-time performance, this study introduces SA-MGhost-DVGG, a novel lightweight network for enhanced detection. The proposed network integrates an MGhost module for reducing parameters and computational load, a mixed-channel spatial attention (SA) module to refine features specific to fiber regions, and depthwise separable convolutions (DepSepConv) for efficient dimensionality reduction while preserving feature information. Experimental evaluations demonstrate that SA-MGhost-DVGG achieves a mean detection accuracy of 99.01% with an average inference time of 2.23 ms per pill. The findings confirm that SA-MGhost-DVGG effectively balances high accuracy with computational efficiency, offering a robust solution for industrial applications.