基于计算机图像识别的垃圾智能分类

Intelligent Garbage Classification Based on Computer Image Recognition

为提高垃圾分类效率,应对日益繁杂的垃圾分类工作,使垃圾分类智能化,高效化。运用卷积神经网络解决垃圾分类问题,对YOLOv3基础算法进行研究改进,并制作垃圾种类数据集,结合参数迁移学习训练垃圾分类识别模型。实验表明样本多的种类识别准确率较高,而对于样本少的种类,准确率就下降了。相较于现有常用的垃圾分类识别算法,所提出的垃圾分类识别算法,识别性能更优,更适合广泛推广应用。

In order to improve the efficiency of garbage classification,deal with the increasingly complicated garbage classification work,and make garbage classification intelligent and efficient.Use convolutional neural network to solve the garbage classification problem,research and improve the basic algorithm of YOLOv3,and make a garbage type data set,combined with parameter transfer learning to train the garbage classification and recognition model.Experiments show that the recognition accuracy of the types with a large number of samples is higher,while for the types with a small number of samples,the accuracy is reduced.Compared with the existing commonly used garbage classification and recognition algorithms,the proposed garbage classification and recognition algorithm has better recognition performance and is more suitable for wide promotion and application.