An intelligent model approach for leakage detection of modified atmosphere pillow bags

Modified atmosphere pillow bags have been widely used to package various food products due to their advantages for preservation and shipment. Sealing defects are statistically inevitable, although modern packaging machinery and manual inspection utilized by manufacturers continue reducing the leakage probability. Hence the bag contents may spoil if the seal is broken. Instead of manual inspection and various destructive methods utilized by factories, this study introduces non-destructive leakage detection using deep learning methods. Firstly, a squeezing method is developed to aggravate the feature difference between positive samples and negative samples without destroying the bag content, thus 2160 images of three different pillow bags are acquired to establish dataset. Secondly, the deep learning model Vision Transformer (ViT) is deployed and studied so that feasibility of computer vision method is verified. Then the Semantic segmentation and Contour Extraction model combining ViT (SCE-ViT) is proposed and improved to the Multi-dimensional Fusion model (SCE-MdF). The accuracies of SCE-MdF reached 97.5%, 97.5%, and 97.5%, respectively. The F1-scores of SCE-MdF reached 97.6%, 97.6%, and 97.4%, respectively. Compared to averaged accuracies of SCE-ViT, accuracies introduced in the ultimate model SCE-MdF improved by 19.17%, 5.84%, and 11.67%, respectively. Therefore, combination of unique squeezing method and Semantic segmentation Contour Extraction with Multi-dimensional Fused ViT, is eventually validated viable on leakage detection of modified atmosphere pillow bags. Hence a cost-effective, efficient and non-destructive leakage detection method for modified atmosphere pillow bags in relevant industry is introduced, filling a gap between artificial intelligence and food packaging industry. • The first time to detect leaky pillow bags using deep learning method, both non-destructively and efficiently. • Leaky pillow bags deformation pattern is verified by Vision Transformer. Features causing low accuracy are found. • Semantic Contour Extraction combining Vision Transformer is developed to exclude unwanted features reaching more accuracy. • Higher stability and accuracy are further ensured by developing SCE-ViT into Multi-viewed 3-dimensional model SCE-MdF.