论文标题
使用CNN图像识别和高斯聚类的可回收废物识别
Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering
论文作者
论文摘要
废物回收是在生产过程中节省能源和材料的重要方法。在一般情况下，可回收的对象与不可缩合的对象混合在一起，这增加了对识别和分类的需求。本文提出了一个卷积神经网络（CNN）模型，以完成这两个任务。该模型使用从预告片的Resnet-50 CNN进行转移学习来完成特征提取。在增强的Trashnet数据集[1]上训练了随后的完全连接层进行分类。在应用程序中，滑动窗口用于分类阶段的图像分割。在分类后阶段，标记的样品点与高斯聚类集成在一起，以定位对象。所得模型的模拟和最终分类精度为92.4％的总体检测率为48.4％。
Waste recycling is an important way of saving energy and materials in the production process. In general cases recyclable objects are mixed with unrecyclable objects, which raises a need for identification and classification. This paper proposes a convolutional neural network (CNN) model to complete both tasks. The model uses transfer learning from a pretrained Resnet-50 CNN to complete feature extraction. A subsequent fully connected layer for classification was trained on the augmented TrashNet dataset [1]. In the application, sliding-window is used for image segmentation in the pre-classification stage. In the post-classification stage, the labelled sample points are integrated with Gaussian Clustering to locate the object. The resulting model has achieved an overall detection rate of 48.4% in simulation and final classification accuracy of 92.4%.
