Packaging and code spraying detection method based on deep learning

Abstract

The invention discloses a packaging and code spraying detection method based on deep learning. The method comprises character zone extraction, single-line character segmentation and single-character identification. The method comprises the following steps that: using a semantic segmentation network to carry out character zone extraction on an image; then, using a column classification network to segment single-line characters; and finally, using a zone classification network to identify the single character. By use of the method, each network is mutually independent and is free from coupling,and finally, final identification accuracy is high. The interference of external bad conditions, including noise, distortion, low contrast, non-uniform illumination and the like, can be reduced to a maximum degree, and the character zone is accurately extracted and can be correctly segmented and identified. Most importantly, the method realizes a purpose that high detection accuracy can be achieved by small sample training, and in addition, the method achieves the requirements of industrial circles on the aspects of detection time consumption and accuracy.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

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AI Technical Summary

Benefits of technology

This patented technique improves upon previous methods for detecting objects like cars. By analyzing an inputted image through specific parts of it's visual content (such as edges) and adjusted its shape accordingly, this system allows accurate identification of car types without relying solely on human judgment. Additionally, these techniques use machine learning models trained over large amounts of images instead of manually annotated ones, making them more efficient than traditional approaches. Overall, this new approach provides improved object detection capabilities compared to current technologies while also reducing their complexity and cost.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving image processing techniques used during printing processes like laser markets due to their complexity and challenges associated with interferential effects caused by various environmental influences including background noises, high levels of ambient brightness, varying degrees of darkness, and variations in color tone values over short periods of time. These issues can lead to errors being detected incorrectly even if they were only slightly changed through observation alone. Therefore, current methods require significant amount of data and trained models may result in slow convergence times.

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