Design of a Garbage Automatic Sorting Machine Based on Machine Vision
Aiming at solving the problem of solid household waste classification,our team propose a scheme for an automatic sorting machine and innovatively design the structure,control circuit,and algorithm.Four matrix fiber optic sensors and two-stage conveyor belts are used to sort garbage.By using the YOLOv5 network feature extraction model and transfer learning,the garbage recognition method is employed to effectively solve the problem of limited garbage datasets.The detection model based on YOLOv5 includes Backbone module,Neck module,Head module,and serial communication module connected in sequence.The method was trained and tested on the self-built garbage data set,which has achieved average accuracy of 0.99.In practical application,the trained model is deployed on the self-developed board and used with the self-made garbage sorting device.Experimental results show that the system can accurately identify the types of garbage and complete the classification and recycling.It takes 15 to 20 seconds to identify around 10 to 15 pieces of garbage since being thrown into the machine,and the machines shows good stability and efficiency in use.
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