Efficient and High-performance Cigarette Appearance Detection Based on YOLOv5

This paper proposes an improved model for two-stage image defect detection in cigarette appearance that enhances both performance and accuracy. The model is based on YOLOv5s and incorporates an attention mechanism. To evaluate the model's effectiveness, we utilized a real appearance defects dataset of cigarettes. Results from the experiments demonstrate that the model can achieve a mean average precision (mAP) of 0.916 and frames per second (FPS) of 82 after 200 epoch. Additionally, in a production environment, the model demonstrated inference performance of 6.5ms (FPS 154). The high detection speed and effectiveness of the model make it suitable for on-site, real-time inspection of cigarette appearance defects detection.