Research and design of a multi-type common item recognition system based on YOLOv5

At present, artificial intelligence and Internet of Things technology has developed very rapidly, and the use of various types of robots and robotic arms has been rapidly popularized. In this case, the market has gradually put forward new requirements for the motion control and visual recognition ability of robots. Based on this current situation, this paper designs a multi-type common object recognition system based on YOLOv5 neural network model, and optimizes the recognition process of the neural network model based on the dynamic mask method for practical application scenarios. The recognition accuracy of the system is improved. The system designed in this paper includes software process design, hardware architecture design and test platform UI design. The tests show that the recognition system designed in this paper requires small hardware computing power, is easy to build, and the recognition accuracy and recognition speed can meet the actual application requirements. At the same time, the test platform can be optimized for different scenarios, which can lay a certain foundation for the subsequent system optimization and other researches.