CS-YOLOv5: An Optimized Tobacco Stem Detection Method

Due to the susceptibility of deep learning-based object detection algorithms to lighting and image quality in cigarette stem detection application scenarios, which leads to the false detection rate and high hardware occupancy. To address this issue, a CS-YOLOv5 cigarette stem detection model with excellent performance is proposed. First, a novel convolutional neural network (ConvNeXt) is used as the backbone network to improve the ability to extract abstract semantic features. Second, after using selective kernel networks (SKNets) with different kernel sizes in three effective special layers, the receptive field size can be adjusted. Finally, we create a tobacco stem dataset and use it to demonstrate that our improved model is excellent at detecting tobacco stems. The performance of the designed model was evaluated by conducting experiments on the tobacco stems dataset, and it showed excellent results.