# A Distributed Architecture for Smart Recycling Using Machine Learning
## 2 Related Work  
MSW is an active multi disciplinary research topic having a wealth of research work. There are social and educational issues and different technological approaches. In this section we pinpoint the most relevant research on the topic of waste classification. The research work of [PERSON] and [PERSON] [26] performed a comparative analysis for classification of images in TrashNet data set where the proposed method used a fine tuned model. Despite the breakthrough implementation, the results show only 83.43% accuracy which is lower than our method. [PERSON] et al. [27] suggest a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high resolution camera to capture waste image and sensors to detect other useful information. The MHS uses a CNN based algorithm to extract image features and a multilayer perceptions (MLP) method to consolidate image attributes and other characteristics information to classify wastes as recyclable or not. The MHS is trained and validated against the manually labeled items, achieving overall classification accuracy higher than 90%. In this study the researchers analyzes a total of two classes recyclable and others. In the research work of [PERSON] et. al [28] were used several CNN architectures for the automatic classification of waste: VGG-16, VGG-19, ResNet, Inception and Inception-ResNet. The experiments were performed on the TrashNet dataset, where the best classification results achieved using a ResNet architecture with 88.66% of average accuracy. [PERSON] et al. [29] proposed an intelligent fusion of Deep Features for improved Waste classification. Utilizing deep architectures, namely AlexNet, GoogleNet, VggNet and ResNet pre-trained on ImageNet, classified the waste at six classes. The best accuracy is 95.58% using the Double Fusion (PSO) method which is worst than ours. [PERSON] and [PERSON] [30] designed two models, one that supports vector machines (SVM) with scale-invariant feature transform (SIFT) features and a convolutional neural network (CNN). The accuracy rate for SVM is 63% and 23% for CNN.The target is to take images of a single piece of recycling or garbage and classify it into six classes consisting of glass, paper, metal, plastic, cardboard, and trash. Similar works as performed by [PERSON] et al. [31] who deployed a project to detect whether an image contains garbage or not. This project employs the pretrained AlexNet model and achieves a mean accuracy of 87.69%. However, this project aims at segmenting garbage in an image without providing functions of waste classification. In our model, the accuracy is 96.57%. In this work the researches divide the waste in two classes garbage and non-garbage. An automated system proposed by [PERSON] et al. [32] based on deep learning approach and traditional techniques by aiming the correct separation of waste in four different recycling categories: Glass, metal, paper and, plastic. The researchers concluded that CNN approaches tends to be more computational expensive than the traditional techniques but requiring better computational resources and the experimental results using VGG-16 methods reached 93% of accuracy in its best scenario which is lower than our algorithm. Moreover, the classification is made in four classes: Glass, paper, metal and plastic. Compared with the related work, our architecture achieves excellent classification accuracy of 97% on five different classes of waste. The proposed system is an end-to-end solution that can identify and provide metrics of industrial stations in real-time via an online platform. The system can achieve 76 frames per second on our modern hardware and is capable of realtime inference on at least four industrial stations. The main novelty of the proposed system architecture is the computation offloading to the cloud. The usage of cloud for waste classification minimizes the implementation cost at larger scales where multiple waste collection facilities use the cloud server.