# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 2 Related Work  
**RVM and CNN**: Various studies have been conducted to solve the limited scope of target resources and the high configuration cost of the RVM system, focusing on classification accuracy, speed, and fraud detection rate. For example, [PERSON] [10] showed that it is possible to process an average of 40.8 objects per minute just using six Raspberry Pi cameras. Although it solved the problem of the high cost of the RVM system, it could not solve the limited scope of collection resources since it is a barcode-based system. [PERSON] and [PERSON] [15] and [PERSON] and [PERSON] [16] also presented sensor-based low-cost RVMs, but they were also constrained by the limited scope of collection resources and the lack of various test cases. These studies indicate that there should be different modalities besides barcodes to broaden the scope of the target objects in the RVM system. In the past decades, with high-performance hardware, such as graphics processing units (GPUs), CNNs have shown groundbreaking results in computer vision, speech processing, or face recognition [17]. The two basic building blocks of CNN are the convolutional layer and the pooling layer. Since the convolutional layer generates an output feature map from the input image by the convolution operation of the convolutional filter, called kernel, it can extract the feature of the input image including spatial information. The pooling layer reduces the size of the feature map by subsampling the input data. This two-fold approach enables the extraction of the spatial-independent global feature of input images [18]. Moreover, since the convolutional layer only needs parameters from a convolutional filter, the number of parameters used in the convolutional layer is significantly less than those in a fully connected layer [19]. For this reason, many studies employed CNNs for the RVM system to widen the scope of the target resources. [PERSON] et al. [12], [PERSON] and [PERSON] [13] introduced an image-based object classification system for PET and aluminum cans. They compared various CNN models such as LeNet, AlexNet, and SqueezeNet, and showed that the best results are derived when LeNet classifies an object into two classes, PET bottles and cans. However, this study is very limited in that it deals with only 15 test cases. [PERSON] et al. [14] proposed a multi-modal network to solve the problem that the single image-based RVM system has difficulty in detecting intentional fraud objects. The modalities used in the study are the image, the weight, and the reflected ultrasound of the object, and they applied the attention module and correspondence learning to the model. The result showed that the model accurately classified the target objects, such as plastic bottles, cans, glasses, and non-target fraud objects. However, since the system integrates modality data with a high-cost controller, the system configuration cost is so high that it may not be an alternative for current RVMs. **Neural Network Ensemble**: The neural network ensemble technique combines neural networks trained for the same purpose [20]. Typical ensemble methods include bootstrap aggregating (bagging) [21], boosting [22], and stacking [23]. These methods can derive a better performance by integrating the predictions of weak classifiers. Ensemble networks can also achieve better results when each network is trained from different datasets and merged [24]. However, integrating every trained classifier does not always produce optimal results [25], and thus we need to choose the classifiers that fit the application. The neural network ensemble is being used in various fields such as image classification and detection to improve performance and make a robust model [26; 27; 28; 29]. [PERSON] et al. [26] classified a channel randomly selected for hyperspectral images and then ensembled each classifier with the majority voting to derive the final result. [PERSON] et al. [27] presented a CNN ensemble model that predicts gender from the face image.