基于模型压缩的安瓿瓶外观检测仿真研究

针对目标检测网络模型规模大、参数量冗余，导致安瓿瓶外观缺陷检测模型难以部署到边缘设备的问题，提出一种基于轻量化网络和模型压缩的Faster R-CNN安瓿瓶外观缺陷检测算法。以MobileNet-V2作为主干网络，利用模型剪枝策略裁剪卷积网络中冗余的通道；通过饱和截取映射将浮点型参数量化为整型；利用知识蒸馏恢复压缩后网络的检测精度。在自主构建的安瓿瓶外观缺陷数据集进行测试，模型体积减少了69.6%，平均精度为89.3%。仿真结果表明：压缩后的目标检测模型满足实际应用中安瓿瓶外观检测要求。

Simulation Research on Appearance Detection of Ampoules Based on Lightweight Network and Model Compression

Aiming at the large scale and redundant parameters of target detection network model, which result in the difficult to deploy the ampoule bottle appearance defect detection model to edge devices, an LC-Faster R-CNN defect detection algorithm based on lightweight network and model compression is proposed. MobileNet-V2 is used as the backbone, and the redundant channels in the convolutional network are trimmed by model pruning strategy. The floating-point parameters are quantized into integers through saturation truncation mapping. Knowledge distillation is used to restore the accuracy of the compressed network. Tested on the self-built ampoule appearance defect dataset, the model volume is reduced by 69.6% and the average accuracy is 89.3%. The simulation results show that the compressed target detection model can meet the requirements of the appearance detection of ampoules in practical applications.