Automatic sorting system based on image classification

In the traditional logistics and distribution process, a large number of tasks cannot be completed accurately and in a timely manner due to errors in manual operations, low efficiency, and high requirements for the working environment. The automated sorting system, by introducing advanced technologies such as image recognition, radio frequency identification (RFID), deep learning, and computer vision, can achieve rapid, accurate, and intelligent item classification and sorting. This ultimately improves delivery efficiency, reduces service costs, and minimizes human errors. This article introduces an automated sorting system based on image recognition and robotic arm grasping. In terms of model selection, we experimented with various deep learning models and ultimately chose InceptionV2 as the final model. To improve the model's accuracy, we adjusted relevant parameters and introduced transfer learning to optimize the model. Experimental results demonstrate that the introduction of transfer learning significantly enhances the model's accuracy, performing well on both the training and testing datasets. Finally, we utilized the CoppeliaSim robot simulation platform to realize the robotic arm's grasping and classification of items on a conveyor belt, completing the closed loop of the automated sorting system.