Lightweight and Efficient Real‐Time Identification of Cigarette Type Based on Improved YOLOV9s

ABSTRACT

The type of tobacco is closely related to the conditions and environment required for its processing and production. Efficiently and accurately identifying the different types of tobacco improves the accuracy of product quality control. Aiming at the current phenomenon of low efficiency of manual identification and screening of tobacco, poor classification effect leading to the difficulty of guaranteeing the quality of cigarettes, as well as the problem that the existing methods are easily interfered with by environmental factors with poor generalization ability in identifying the type of tobacco, and the accuracy is not high, a method of determining the type of tobacco components of the Golden Leaf based on the improvement of YOLOV9s was proposed. First, the original backbone network was replaced with FasterNet to reduce channel redundancy and model complexity while maintaining comparable detection performance. This lightweight backbone provides a computationally efficient foundation for subsequent feature enhancement modules. Second, in the feature extraction part, variable kernel convolution AKConv was used to replace the original traditional convolution, which utilizes a flexible convolution mechanism to keep the model lightweight and enhance the accuracy of target information feature extraction simultaneously. Finally, the downsampled convolutional AConv of the neck was replaced with HWD (Haar wavelet downsampling) to expand the receptive field and reduce the loss of important spatial information. It was experimentally verified that compared to the original YOLOV9s algorithm, the improved algorithm improved recall and mAP by 2.33% and 1.1%, respectively, and reduced the number of references and memory footprint by 1.16 × 10 ⁶ and 2.22 MB, respectively. YOLOV9s‐RepNCSPELAN4_AKConv‐HWD was 1.92%, 2.82%, 2.67%, and 5.76% higher in mAP than other mainstream models. The results showed that the algorithm struck a good balance between detection accuracy and lightweight, providing a reliable reference with high accuracy and real‐time performance for deployment on local devices.