Research on Printing Defect Detection of Flexible Packaging Based on Improved Faster-RCNN Algorithm
This paper proposes an improved Faster-RCNN algorithm for defect detection, and the original VGG16 network is replaced by a residual network (ResNet-50) with smaller computation and deeper network depth for the characteristics of missing prints in flexible packaging, which can extract rich features. In order to make the convolutional neural network adaptive attention, the CBAM self-attention mechanism module was added to the residual network of ResNet-50. For datasets, the collected images are enhanced by rotation, translation, brightness adjustment, noise addition, cutout, and other operations to avoid unbalanced data samples and improve the robustness of the model. The results show that the precision of improved Faster-RCNN model is 12％ higher than the unimproved Faster-RCNN model, and the mAP reaches 92.95％. Prove the effectiveness of the improved model, save a lot of labor costs, and improve the production efficiency of the enterprise.
万方数据
维普
