目的 针对传统的基于人工的腌制蔬菜真空缺陷包装剔除效率低、漏检率高等问题，提出一种基于改进YOLOv5s的腌制蔬菜真空包装缺陷检测方法。方法 首先，使用Ghost卷积替换CSP模块中的卷积，在提高模型特征提取能力的同时降低网络的参数量;其次，利用空间换深度(Space-to-Depth, SPD)和深度可分离卷积(Depthwise-Separable Convolution, DSConv)组合操作SPD–DSConv进行下采样，减少下采样造成的特征信息损耗;最后，在网络中引入SE注意力机制，提高算法的精确率。结果 在自制的腌制蔬菜真空包装数据集上，改进后的网络平均精度(man Average Precision, AmAP)为93.88%，模型尺寸为3.91 MB，相比原网络精度提高了2.05%，模型尺寸缩减了44.38%。结论 文中方法能够实现腌制蔬菜真空缺陷包装的分类和定位，为基于机器人的缺陷包装剔除奠定了基础。

The work aims to propose a vacuum packaging defect detection method for pickled vegetables based on YOLOv5s network to solve the low efficiency and high leakage rate of manual-based vacuum defect packaging rejection of pickled vegetables. Firstly, Ghost Convolution was used to replace the convolution in the CSP module, which reduced the number of parameters in the network while improving the feature extraction capability of the model; Secondly, in order to reduce the loss of feature information in down sampling, the space-to-depth (SPD) and depthwise-separable convolution (DSConv) were used in down sampling; Finally, the SE attention mechanism module was introduced in the network to improve the accuracy of the algorithm. On the dataset of homemade pickled vegetable packaging, the mean average precision (AmAP) of the improved network reached 93.88 and the model size reached 3.91 MB. Compared with the original model, the mAP was increased by 2.05% and the model was reduced by 44.38%. The method in the paper enables the classification and localization of the defective vacuum packages of pickled vegetables, and lays a foundation for robot-based defective package rejection.