Design of an Intelligent Garbage Recognition and Classification System Based on K210 and Deep Learning Algorithm

DOI: https://doi.org/10.62517/jes.202602101

Author(s)

Ming Yang, Hao Ma, Xu Chen, Ningye He*

Affiliation(s)

School of Information Engineering, Huangshan University, Huangshan, Anhui, China *Corresponding Author

Abstract

With the acceleration of urbanization, the problem of "garbage besieging cities" is becoming increasingly severe. Traditional manual sorting methods suffer from low efficiency, high cost, and unstable accuracy. This paper designs and implements an intelligent garbage recognition and classification system based on the K210 edge AI computing chip and deep learning algorithms. The system uses the K210 as the core computing unit, equipped with an OV2640 camera for image capture. It utilizes a lightweight MobileNetV1 model, optimized through pruning and quantization, deployed on its KPU for real-time local inference. Recognition results are fed back via a TFT display and drive SG90 servos to perform sorting actions. Test results show that the system achieves an average recognition accuracy of 92.5% for eight common types of recyclable waste, with an average response time of less than 1.5 seconds. It features high recognition accuracy, fast response speed, and stable, reliable operation, providing an efficient and feasible embedded solution for the intelligent and automated classification of garbage.

Keywords

K210; Deep Learning; Garbage Recognition; Edge Computing; Embedded System

References

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