College of Landscape Architecture and Art, Northwest A&F University, Xianyang 712100, China.
Comput Math Methods Med. 2022 Jun 2;2022:6554371. doi: 10.1155/2022/6554371. eCollection 2022.
With development of economy, all industries have undergone earthshaking changes. Various new technologies are starting to be employed in all aspects of life, and graphic design is no exception. The use of computer graphics and image processing technologies in graphic design can substantially improve design efficiency and make graphic design job more convenient to develop. The requirements for the quality of graphic design are higher. Quality inspection has become a necessary step in the production process, in which the detection of graphic design defects is an indispensable and important link. The traditional graphic 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, convolutional neural 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 application technology for graphic design defect detection with convolutional neural network (CNN). 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. The related improvement techniques are then given, based on the characteristics of agricultural product packaging design faults. The backbone network, multiscale feature map, a priori frame, and activation function of YOLOv3 are improved, and then performance of the improved model is verified by experiments.
随着经济的发展，各行各业都发生了翻天覆地的变化。各种新技术开始应用于生活的方方面面，图形设计也不例外。在图形设计中使用计算机图形和图像处理技术，可以大大提高设计效率，使图形设计工作更加便捷。对图形设计质量的要求也越来越高。质量检查已成为生产过程中的必要步骤，其中图形设计缺陷的检测是不可或缺的重要环节。传统的图形设计缺陷检测采用人工视觉检测的方法，存在稳定性差、耗时长、人工成本高等缺点。卷积神经网络作为一种高效的计算机图形和图像处理技术，由于其高速、高效、高度自动化的优点，在图形设计缺陷检测中受到了广泛关注。以农产品包装为例，本文研究了基于卷积神经网络（CNN）的图形设计缺陷检测应用技术。主要内容如下：构建原始的 YOLOv3 网络模型，根据硬件设备的计算能力，批量输入农产品包装的图形设计图像，训练 YOLOv3 网络，并对实验结果进行深入研究和分析。然后，根据农产品包装设计缺陷的特点，给出相关的改进技术。对 YOLOv3 的骨干网络、多尺度特征图、先验框和激活函数进行改进，然后通过实验验证改进模型的性能。
