论文标题
使用更快的R-CNN对象进行分类
Object sorting using faster R-CNN
论文作者
论文摘要
在工厂生产线中，需要快速区分和分类不同的行业零件以进行进一步的过程。零件可以具有不同的颜色和形状。人类将这些对象分为适当的类别是很乏味的。自动化此过程将节省更多的时间和成本。在自动化过程中，选择适当的模型来检测和根据特定功能对不同对象进行分类更具挑战性。在本文中，将三种不同的神经网络模型与对象分类系统进行了比较。它们是CNN，快速R-CNN和更快的R-CNN。这些模型进行了测试，并分析了它们的性能。此外，对于对象排序系统，将Arduino控制的5 DOF（自由度）机器人组进行编程，以将对称对象抓住并将对称对象丢弃到目标区域。对象根据颜色，缺陷和非缺陷对象分类为类。
In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.
