A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning

TL;DR

This paper presents a system combining deep learning and structured light 3D reconstruction to improve the recognition and sorting of food packages by robotic arms, especially handling transparent and reflective materials.

Contribution

It introduces a novel integration of MASK R-CNN and structured light technology for accurate 3D positioning of food packages, overcoming limitations of traditional vision methods.

Findings

High accuracy recognition and grasping demonstrated

Effective handling of transparent and reflective packages

Automated 3D coordinate calculation for robotic grasping

Abstract

Vision algorithm-based robotic arm grasping system is one of the robotic arm systems that can be applied to a wide range of scenarios. It uses algorithms to automatically identify the location of the target and guide the robotic arm to grasp it, which has more flexible features than the teachable robotic arm grasping system. However, for some food packages, their transparent packages or reflective materials bring challenges to the recognition of vision algorithms, and traditional vision algorithms cannot achieve high accuracy for these packages. In addition, in the process of robotic arm grasping, the positioning on the z-axis height still requires manual setting of parameters, which may cause errors. Based on the above two problems, we designed a sorting system for food packaging using deep learning algorithms and structured light 3D reconstruction technology. Using a pre-trained MASK R-CNN model to recognize the class of the object in the image and get its 2D coordinates, then using structured light 3D reconstruction technique to calculate its 3D coordinates, and finally after the coordinate system conversion to guide the robotic arm for grasping. After testing, it is shown that the method can fully automate the recognition and grasping of different kinds of food packages with high accuracy. Using this method, it can help food manufacturers to reduce production costs and improve production efficiency.