论文标题
对废物分类的多个深CNN模型的比较分析
Comparative Analysis of Multiple Deep CNN Models for Waste Classification
论文作者
论文摘要
废物是错误的地方的财富。 Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process.正在实践各种方法来管理废物，但不高效，需要人类干预。自动废物隔离将适合填补空白。 The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net.卷积神经网络用于图像分类。以垃圾箱形式构建的硬件用于将这些废物隔离为不同的隔间。 Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management.市政固体废物是一种巨大的可再生能源。对于政府，社会和工业家而言，情况都是双赢的。由于RESNET18网络进行了微调，发现最佳验证精度为87.8％。
Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuning of the ResNet18 Network, the best validation accuracy was found to be 87.8%.
