Online Detection Method for Flatness of Cigarette Packaging Aluminum Foil Paper by Integrating Line Laser Projection and YOLOv7

YANG Caihong; YI Jincan; ZHU Kailin; PU Shaohu; WANG Tantan; GUO Hao; JI Changwen; CAI Peiliang

doi:10.19554/j.cnki.1001-3563.2026.05.021

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (5) : 190-199. DOI: 10.19554/j.cnki.1001-3563.2026.05.021

Online Detection Method for Flatness of Cigarette Packaging Aluminum Foil Paper by Integrating Line Laser Projection and YOLOv7

YANG Caihong¹, YI Jincan¹, ZHU Kailin¹, PU Shaohu¹, WANG Tantan¹, GUO Hao¹, JI Changwen², CAI Peiliang^1,*

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Abstract

The work aims to deal with the challenges of online inspection for wrinkles, breaks, and other surface defects on cigarette package aluminum foil arising from its highly reflective and flexible nature, as well as the high false detection rates of traditional visual inspection methods and the insufficient generalization of deep learning algorithms in complex working conditions. A dual-light source visual inspection method that integrated line laser projection statistics with the YOLOv7 object detection algorithm was proposed. Firstly, an imaging system consisting of a white light source and a red line laser was constructed. Based on the triangulation principle, microscopic height variations on the foil surface were converted into pixel displacements of the laser line. Subsequently, by extracting projection statistical features (e.g., standard deviation, gradient) of the laser line centerline, a rapid defect classification rule was established to achieve preliminary screening of typical defects such as wrinkles and breaks. Finally, the YOLOv7 deep learning model was introduced, leveraging its Extended Efficient Layer Aggregation Network (ELAN) and Reparameterized Convolution (RepConv) structures to accurately locate and identify complex defects, forming a dual detection mechanism of statistical preliminary screening and deep learning refined judgment. Industrial experimental results demonstrated that the system operated stably at a production speed of 400 packs per minute, achieving a defect detection rate exceeding 99.9%. The number of defective packs returned from downstream processes was reduced from a daily average of 1.6 before installation to zero, while the false removal rate remained very low. This research effectively addresses the problem of defect detection under strong interference from aluminum foil reflections, providing a reliable, high-precision, and highly robust solution for online quality control in tobacco packaging.

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aluminum foil defects / line laser projection / projection statistics / YOLOv7 / online inspection / machine vision

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YANG Caihong, YI Jincan, ZHU Kailin, PU Shaohu, WANG Tantan, GUO Hao, JI Changwen, CAI Peiliang. Online Detection Method for Flatness of Cigarette Packaging Aluminum Foil Paper by Integrating Line Laser Projection and YOLOv7[J]. Packaging Engineering. 2026, 47(5): 190-199 https://doi.org/10.19554/j.cnki.1001-3563.2026.05.021

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