基于机器视觉与深度学习的烟用商标纸质量评价方法研究OA北大核心

To investigate the application of machine vision technology integrating deep learning in evaluating the quality inspection results of cigarette trademark paper,this study proposed a comprehensive evaluation method.A high-resolution industrial camera,customized light sources,and specialized software systems were employed to construct an annotated dataset.Dynamic threshold ORB feature detection,opti-mized RANSAC registration,and multi-band fusion strategies were adopted,effectively eliminating stitching seams.The images PSNR of 38.9 dB and SSIM of 0.94.For feature recognition,the YOLOv8 model was enhanced by introducing a CBAM attention module,combined with a ResNet-34 backbone network and FPN multi-scale feature fusion,achieving 99.4%mAP50,99.6%recall,and 99.0%precision on the test set.A dual-branch Siamese network was designed to compute similarity by fusing SIFT descriptors and deep semantic features,achieving average recognition accuracies of 97.64%for small box trademark paper and 95.85%for carton trademark paper.