Hyperspectral Images-Based Stem Sticks Signature Detection of Cut Tobacco Using Improved YOLOv8n Algorithm

Abstract

The tobacco industry attaches great importance to the development of slim cigarettes, and the content of stem sticks in slim cigarettes is extremely important to the quality of cigarettes. Therefore, in order to solve the problem of difficult detection of stem sticks in cut tobacco, a stem sticks detection algorithm in cut tobacco based on hyperspectral image technology combined with improved YOLOv8n is proposed. First, a principal component analysis method was used to process the hyperspectral image data to improve the differentiation between cut tobacco and stem sticks, and to construct the dataset. Second, the YOLOv8n algorithm was optimized to obtain the GMCM-YOLOv8n algorithm. Multiscale convolutional attention was introduced in the backbone network to capture detail information. Then, ghost convolution (GhostConv) was introduced to replace the regular convolution to simplify the network. M-BiFPN modules are proposed in neck networks as a way to improve the detection of small-sized stem sticks. The C2f module is also improved to obtain P-C2f with a view to reducing the model parameters and computational volume. Finally, the effectiveness of the GMCM-YOLOv8n algorithm is experimentally verified on self-constructed dataset. The results of the experiment showed that: the algorithm achieved a mean average precision of 93.9%, with parameters and floating point operations of 2.2 M and 6.2 G, respectively, and frames per second maintained at 73.5 fps. Compared with YOLOv8n, the proposed improved algorithm exhibited better comprehensive performance, which provided a valuable reference for realizing the task of quickly and accurately detecting the content of stem sticks in cut tobacco in practical production.