Research on cigarette category recognition technology based on improved YOLOv7

To overcome the challenges encountered by the cigarette industry,characterized by high workload,low efficiency,and high time and cost in inspection and inventory management,this study explores the application of deep learning-based cigarette product classification technology. Initially,10 000 images of cigarette inventory were collected from the field to establish the first common cigarette product image recognition dataset,termed CCD-12K. Subsequently,a novel model named YOLOv7-Cr was proposed,which integrates deformable convolution DCNv2 and self-attention mechanisms for cigarette product detection. The model incorporates deformable convolution into the feature extraction network to enhance the model’s receptive field and its capacity to discern shape variations. Additionally,it employs BRA attention to aggregate key features from cigarette images,calculating the information of both foreground and background features to improve the network's discrimination ability between foreground and background. The test results demonstrate that on the CCD-12K dataset,YOLOv7-Cr achieved an average precision (mAP) of 92.3% for cigarette product recognition,representing a 2.9 percentage point improvement over YOLOv7,effectively fulfilling the requirement of mean accuracy exceeding 90% in law enforcement applications.