Application of Graphic Design with Computer Graphics and Image Processing: Taking Packaging Design of Agricultural Products as an Example
Author: Tianhe Xie, Rongyi Sun, Jiahao Zhang, Ruiqi Wang,
and Jiashu Wang
Date: June 2022
OVERVIEW
With development of economy, all industries have undergone earthshaking changes. Various new technologies are starting to beemployed in all aspects of life, and graphic design is no exception. The use of computer graphics and image processingtechnologies in graphic design can substantially improve design efficiency and make graphic design job more convenient todevelop. The requirements for the quality of graphic design are higher. Quality inspection has become a necessary step in theproduction process, in which the detection of graphic design defects is an indispensable and important link. The traditionalgraphic design defect detection adopts the method of manual visual inspection, which has the disadvantages of poor stability,long consumption time, and high labor cost. As an efficient computer graphics and image processing technology, convolutionalneural network has received extensive attention in graphic design defect detection because of its advantages of high speed,efficiency, and high degree of automation. Taking agricultural product packaging as an example, this paper studies applicationtechnology for graphic design defect detection with convolutional neural network (CNN).
STATEMENT OF THE PRBOLEM
The main contents are as follows: construct the original YOLOv3 network model, input the graphic design images of agricultural product packaging into the network model in batches according to the computing power of the hardware equipment, train the YOLOv3 network, and deeply study and analyze the experimental results.
TARGET AUDIENCE
This paper is part of the provincial “University Student Innovation and Entrepreneurship Training Program”project of Northwest A&F University: the research results of “Mintian Agricultural Products Marketing Planning Studio”
RESULT
this study examines the use of aconvolutional neural network as a computer graphics andimage processing technology in defect identification in graphicdesign, as well as the current position. To complete theenhancement of the YOLOv3 network model, this work usesthe packaging design of agricultural products as an example.The network model is presented to improve the direction ofthis topic based on the experimental findings of the originalYOLOv3 and an in-depth analysis of the characteristics of var-ious agricultural product packaging graphic design faults. Itincludes the improvement of the backbone network,