Research on Defect Detection in Aluminum-Plastic Blister Packaging of Pharmaceutical Products Based on Multi-Layer Perceptron
Pages 214 - 219
Abstract
In order to solve the problem of poor detection effect caused by color, size and picture noise of aluminum-plastic blister capsules, a method for detecting packaging defects of aluminum-plastic blister capsules was proposed based on multi-layer perceptron model. First, the batch number area of the drug board was used as the template, and the normalized product correlation gray level was used to match the legal position of the drug board to be detected. Then, the capsule blister region of the drug board was divided by the improved gray value projection method, and the gray value of the drug blister region, capsule volume and boundary characteristics were extracted. The multi-layer perceptron model was trained and tested to realize the identification of packaging defects such as missing, double cap and concave cap of aluminum-plastic blister drugs. Experimental results show that the improved horizontial-vertical projection algorithm can segment the capsule bubble cap region with 100% accuracy, high robustness and good segmentation effect. The identification accuracy of missing capsules, double caps and concave is high, and the average detection time is 4.47ms/ sheet, which can meet the quality inspection task of aluminum blister medicine board packaging.
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Published: 26 March 2024
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EBIMCS 2023
EBIMCS 2023: 2023 6th International Conference on E-Business, Information Management and Computer Science
December 30 - 31, 2023
Hong Kong, Hong Kong
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