Cigarette appearance online detection method and device
A detection method and technology for cigarettes, which are used in measurement devices, optical testing of flaws/defects, material analysis by optical means, etc., can solve the problem that cigarettes are prone to misjudgment, appear on two images at the same time, and cannot be identified. Droplets and other problems, to achieve the effect of ensuring the consistency of the entrance feeling
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Embodiment 1
[0114] In this embodiment, the comparison algorithm of the existing cigarette appearance online detection system cannot realize the defect detection of the patterned cigarette area, and the method of eliminating droplets can only reduce the interference of droplets, but cannot identify droplets and eliminate interference. problem, the specific method is as follows:
[0115] S1: The third camera component collects 1,000 images of no defects on the circumferential surface, images of droplets, images of damaged defects, images of stains, and images of foam defects, totaling 5,000 images, which are divided into training sets and prediction sets at a ratio of 4:1;
[0116] S2: Use the threshold segmentation algorithm to separate the background to obtain the cigarette image, and then correct the cigarette image through template matching to ensure that the cigarette is in the same position and direction of each image;
[0117] S3: Divide each image into three sections, namely cigaret...
Embodiment 2
[0124] This embodiment is an alternative solution for using a deep learning model to detect defects on the appearance of the end face of the cigarette holder. The specific implementation method is as follows:
[0125] S1: Collect 1,000 images of non-defective cigarettes on the end face, images of droplet interference, images of roundness defects, images of damage defects, images of stain defects, and images of foam defects, a total of 6,000 images, which are divided into training set and training set at a ratio of 4:1. prediction set;
[0126] S2: Use the threshold segmentation algorithm to separate the background to obtain the mouthpiece image, and calculate the center position of the mouthpiece image, and further determine the region of interest (Region of interest, ROI) of the mouthpiece to be detected;
[0127] S3: Build the GoogLeNet model under the Caffe platform, bring the ROI of the 6 types of images in the training set into the GoogLeNet model for training, and use th...
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