基于Python的机器视觉实验教学平台设计

DESIGN OF MACHINE VISION EXPERIMENT TEACHING PLATFORM BASED ON ARTIFICIAL INTELLIGENCE

摘要:

针对机器视觉领域的学习内容抽象、难以理解，相关的实验教学产品不足这些问题，基于Python语言，采用开源的opencv-python图像处理库与TensorFlow机器学习框架，提出构建机器视觉实验教学平台。该系统涵盖机器视觉的经典方法，主要包括向量机、K临近图像分类，神经网络、卷积神经网络目标识别，基于经典方法融合常用函数，对系统分模块设计。经过测试，该系统具有较好的交互性与可扩展性，可以适应机器视觉的实验要求，训练数据、样本测试数据导入灵活，机器视觉参数优化、代码迭代方便，并且能够编译生成.exe可执行文件，辅助学生学习机器视觉技术的真实应用场景，提高学生实践解决问题能力和创新能力。

Abstract:

The learning content in the field of machine vision is abstract and difficult to understand, The related experimental teaching products are insufficient, Based on the Python language, the open-source opencv-python image processing library and the TensorFlow machine learning framework are used to construct a machine vision experimental teaching platform. The system covers the classical methods of machine vision, including vector machine, K proximity image classification, neural network, convolutional neural network target recognition, fusion of commonly used functions based on classical methods, and system sub-module design. After testing, the system has good interactivity and scalability, can adapt to the experimental requirements of machine vision, training data, sample test data import flexibility, machine vision parameter optimization, code iteration is convenient, and can compile and generate .exe executable The document assists students in learning the real application scenarios of machine vision technology and improving students' ability to solve problems and innovate.