基于YOLO网络模型的多类别标签缺陷检测

Detection of multi-class label defects based on YOLO network model

由于热转印滚筒温度的变化,热转印标签在转印的过程中会出现热转印标签褶皱、脱模不完全等问题。针对某些较大标签存在缺陷种类较多及未知缺陷的问题,提出了一种基于YOLO网络模型的多类别标签缺陷检测方法,将自适应匹配缺陷检测方法与改进的YOLO网络模型相结合,增加注意力机制模块以提高小目标缺陷的检测能力。在处理过程中,首先,对不同区域内的标签进行快速定位及预处理;然后,针对不同区域使用不同的检测方法进行检测;最后,将不同区域结果融合,判断检测结果。试验结果表明,基于YOLO网络模型的多类别标签缺陷的检测方法能够有效进行热转印标签的缺陷检测,检测准确率达98%,能够满足实际的生产要求。

During the use of heat transfer labels,due to changes in the temperature of the heat transfer drum,problems such as wrinkles and incomplete demolding of the heat transfer labels may occur during the transfer process.In the problem of defect detection in heat transfer printing,some larger labels have many types of defects and some unknown defects.Therefore,a multi-class label defect detection method based on YOLO network model was proposed.The self-adaptive matching defect detection method was combined with the improved YOLO network model for detection,and the YOLO network model was added with an attention mechanism to improve the model's ability to detect small target defects.During the processing,first,the labels in different regions were quickly located for preprocessing;Then,different regions were detected using different detection methods;Finally,the results of different regions were fused to determine the detection results.The results showed that the multi-class label defect detection method based on the YOLO network model can effectively detect defects in heat transfer labels,with an overall detection accuracy of 98%,which can meet practical production requirements.