| 摘 要: 为解决使用普通垃圾桶时采用人工分拣垃圾造成垃圾分类不准确、效率低下的问题，设计了一款基于树莓派的智能分类垃圾桶。该垃圾桶的分类算法基于TensorFlow架构，采用全卷积网络(Fully Convolutional Networks,FCN)实现对垃圾图像特征的学习和识别，用于训练数据集以改进图像识别的准确率，并明确垃圾所属分类。实际应用时，利用传感器采集数据，利用摄像头识别物品，利用舵机带动投放口到正确的分类位置投放垃圾，并且语音播报当前的垃圾种类。本研究共收集了五类垃圾图像，每类图像训练34 组，每组150 次。实验结果表明，该智能垃圾桶的分类准确率可达到85%以上，具有较好的分类效果。 |
| 关键词: 垃圾分类；树莓派；TensorFlow；FCN；深度学习 |
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中图分类号: TP399
文献标识码: A
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| 基金项目: 陕西省大学生创新创业训练项目(S202110722078). |
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| Design of a Intelligent Classification Garbage Can based on Raspberry Pi |
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JIN Lijuan, LIU Qichang, ZHANG Xuru, LIU Junjie
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(Department of Computer Science, Xianyang Normal University, Xianyang 712000, China)
1670479401@qq.com; 1360217647@qq.com; 2413515102@qq.com; 1649004214@qq.com
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| Abstract: Aiming at the problem of inaccurate garbage classification and low efficiency caused by manual classification of garbage when using ordinary garbage cans, this paper proposes to design an intelligent classification garbage can based on Raspberry Pi. Its classification algorithm is based on TensorFlow architecture, and FCN (Fully Convolutional Networks) is used to realize the learning and recognition of garbage image features, which is used to train the data set to improve the accuracy of image recognition and clarify the classification of garbage. In practical applications, sensors are used to collect data, cameras to identify items, steering gears to drive the discharge port to the correct classification position, and the current type of garbage is broadcast by voice. In this study, a total of 5 types of garbage images are collected. Each type of image is trained for 34 groups, 150 times in each group. The experimental results show that the classification accuracy of the intelligent garbage can reaches more than 85%, which has a good classification effect. |
| Keywords: garbage classification; Raspberry Pi; TensorFlow; FCN; deep learning |
