基于改进YOLOv5s 的药盒钢印日期识别方法

Improved YOLOv5s-based Date Recognition Method for Steel Stamps on Pill Boxes

目的药盒的钢印日期与背景对比度低,字符轮廓不明显,识别易受环境光线干扰,对此提出一种基于机器视觉的识别方法。方法使用改进YOLOv5s模型,首先对采集的药盒数据集进行透视变换校正,并进行数据增强。通过在模型的骨干网络中融合位置注意力机制(CA),减少冗余信息的干扰;颈部网络根据加权双向特征金字塔网络(BiFPN)引入权重,更好地平衡不同尺寸图层的特征信息;引入动态聚焦损失函数(WIoU),降低高质量样本对训练的干预,提高模型的泛化能力。结果在自建钢印字符数据集上的实验结果表明,改进网络对药盒钢印日期识别的平均精度值达到了99.41%,比原始模型提升了2.38%,帧率为80.01帧/s。结论改进后的YOLOv5模型对药盒钢印日期的检测精度优于原有网络,对可以满足药盒生产线的实时性要求。

The work aims to propose a machine vision-based recognition method for pill boxes with low contrast between the steel-stamped date and the background,inconspicuous character outlines,and recognition susceptible to interference by ambient light.An improved YOLOv5s model was used to correct the collected pill box dataset by perspective transformation and data enhancement.By fusing the Coordinate Attention(CA)in the backbone network of the model,the interference of redundant information was reduced.The neck network introduced weights according to the Bi-directional Feature Pyramid Network(BiFPN)to better balance the feature information of the layers of different sizes.The Wise-IoU(WIoU)was introduced to reduce the intervention of high-quality samples in the training and to improve the model's generalization ability.The experimental results on the self-constructed steel-stamped character dataset showed that the average accuracy of the improved network for recognizing the steel-stamped date of the pill box reached 99.41%,which was 2.38%higher than that of the original model,and the frame rate was 80.01 f/s.The improved YOLOv5 model can detect the steel-stamped date of the pill box with a better accuracy than that of the original network,and it can meet the real-time requirement of the production line of the pill box.