Portioning Algorithm Using the Bisection Method for Slicing Food

Jetnipat Thongprasith, Poom Separattananan, Phumrpee Meyer, R. Chanchareon
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引用次数: 2

Abstract

Food is an essential part of human life and plays a crucial role in maintaining good health and well-being. In various industries, such as food processing and packaging, it is essential to ensure that raw materials are divided equally to optimize the production process and reduce waste. However, traditional methods of food processing and packaging can be time-consuming and prone to errors. Hence, we are interested in developing a method for accurately portion materials into equal sizes using the Intel RealSense D435i 3D camera to capture point cloud images of object, which are then processed using Python code, running on a Raspberry Pi 4, to generate cutting planes. In the experiment on object size variations, three sizes of plasticine weighing 50 g, 150 g, and 250 g. resulting in errors of 10.2%, 8.8%, and 7.3%, respectively. In the experiment on the number of cutting plane variations, keeping the object weight fixed at 150 g at 150 g, and divided into 2, 3, 4, and 5 pieces. The resulting errors were 1.3%, 8.8%, 10.7%, and 18.2%, respectively, according to the number of pieces. Our algorithm can generate precise cutting planes to partition the volume of an object. The primary cause of errors is the shape resolution of the object's point cloud that the camera can collect and the use of human hands for cutting the object.
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用等分法对食物进行切片的分割算法
食物是人类生活中必不可少的一部分,在保持身体健康和幸福方面起着至关重要的作用。在各个行业,如食品加工和包装,必须确保原材料平均分配,以优化生产过程,减少浪费。然而,传统的食品加工和包装方法既费时又容易出错。因此,我们有兴趣开发一种方法,使用英特尔RealSense D435i 3D相机准确地将材料分成等大小,以捕获物体的点云图像,然后使用Python代码处理,在树莓派4上运行,以生成切割平面。在物体尺寸变化的实验中,选用50g、150g和250g三种尺寸的橡皮泥,误差分别为10.2%、8.8%和7.3%。在实验中对切割平面的数量变化,保持物体重量固定在150g,并分为2、3、4、5块。结果显示,按片数计算,误差分别为1.3%、8.8%、10.7%和18.2%。我们的算法可以生成精确的切割平面来划分物体的体积。产生误差的主要原因是相机可以收集到的物体点云的形状分辨率和人工切割物体的使用。
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