Defect detection algorithm for cigarette outer packaging based on deep learning

In the cigarette production process, detecting defects in the outer packaging is a crucial step. To address the issue of high false recognition rates in factory environments, we propose an improved YOLOv7-tiny target detection algorithm. Our algorithm is based on the YOLOv7-tiny network structure and enhances feature extraction and location by adding the SA module at the end of the Backbone. Additionally, we integrate the GSConv module into the network Neck part to improve information flow and feature fusion ability. Our experimental results show that adding the SA module and the GSConv module to the network increases average accuracy by 2.2% and 3.5%, respectively. After fusing the two, the average detection accuracy is 94.6%. Compared to the original YOLOv7-tiny, our improvements result in a 5.4%increase in accuracy.