关键词: 改进YOLOv11, 卷烟仓储人员, 不安全行为, 去噪, 目标检测

Abstract:

To ensure the safety of personnel and property within the storage environment, the traditional YOLOv11 object detection algorithm was improved, and a method and model to identify unsafe behaviors of personnel in the complex environment of tobacco warehouses were proposed. First, a statistical analysis of common unsafe behavior types in tobacco storage was conducted, and the classification of unsafe behaviors of warehouse personnel was explored, including item-related, action-related, and area-related unsafe behaviors. Second, based on the characteristics of unsafe behaviors of warehouse personnel, a dataset augmentation and denoising preprocessing approach was proposed to enhance fine-grained feature extraction, and introduced to improve the saliency mapping of personnel behaviors. Then, the YOLOv11 algorithm was improved through functional enhancement modules and K-means++ anchor box optimization, and a fast detection method for unsafe behaviors of tobacco warehouse personnel was proposed. Finally, the proposed method's effectiveness was validated by comparing with self-built datasets and the open Microsoft COCO dataset. The results show that the method can quickly and effectively identify unsafe behaviors of warehouse personnel, with a significant improvement in recognition accuracy compared to traditional methods(accuracy rate is 94.91% and 88.69% respectively).

Key words: improved YOLOv11, tobacco warehouse personnel, unsafe behaviors, denoising, object detection