Real-Time Assessment of Cut Tobacco Alignment Orderliness Using an Improved Single-Stage Regression Model

Cut tobacco orderliness, defined as the alignment consistency of cut tobacco along the cigarette axis, is a critical factor in production quality optimization. Accurate and automated assessment of this property remains challenging due to complex strand structures and variable imaging conditions. An improved single-stage regression model is developed to enable reliable detection of cut tobacco alignment orderliness. The approach incorporates three architectural modifications: a multifrequency interactive downsampling method to preserve feature information, a triple complementary fusion strategy to enhance multiscale feature representation, and a dynamic detection head to improve localization across different scales. In addition, a custom orientation prediction method is implemented to quantify alignment orderliness. The model is evaluated on an industrial image dataset, achieving a mean average precision of 89.9%, a precision of 90.6%, and a recall of 88.7%. Compared with the baseline model, the proposed approach reduces model parameters by 30.8% (from 2.6M to 1.8M) and computational complexity by 21.5% (from 6.5G to 5.1G), while improving performance. Overall, the method enables efficient and accurate assessment of cut tobacco alignment orderliness and demonstrates suitability for real-time industrial applications.