Purpose: Addresses the pressing public health concern of tobacco product portrayal on social media, which significantly influences the younger demographic by glamorizing smoking culture. The purpose is to compare the capabilities of YOLOv5 and YOLOv8 models in detecting and censoring cigarette-related imagery on social media platforms, aiming to reduce exposure among children and teenagers. Methods: Employing a dataset of 2,188 images collected from Twitter, this research undertook a comprehensive methodology involving data preprocessing, YOLOv5 and YOLOv8 model training, and rigorous evaluation. The study utilized mean Average Precision (mAP) and F1-Score metrics to evaluate the performance of YOLOv5 and YOLOv8 models, focusing on their precision, recall, and efficacy in detecting cigarette and cigarette pack objects. Result: The analysis highlighted YOLOv8's superiority, with a marginally higher mAP value of 0.933 compared to YOLOv5's 0.919, alongside enhanced precision and recall rates. This result underscores YOLOv8's advanced object detection capabilities, owing to its architectural innovations and anchor-free detection system. Additionally, the study confirmed the absence of significant overfitting or underfitting issues, indicating robust learning processes of the models. Novelty: Innovates in digital public health by using YOLOv5 and YOLOv8 models to automatically censor tobacco-related content on social media, effectively reducing youth exposure to such imagery. YOLOv8, in particular, exhibits marginally superior detection capabilities. The evaluation results surpass those of previous research on cigarette and cigarette burning detection, underscoring the study's significant contribution to future research and public health initiatives.
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