# Smart Trash Bin Model Design and Future for Smart City
## 2 Related Research
### Trash Image Classification  
Along with the research on smart trash bin models, there are also studies on trash classification for segregated collection. Many studies have classified trash images using various machine learning algorithms, including the Convolutional Neural Network (CNN). [PERSON] et al. [24] used a Support Vector Machine (SVM) for trash detection and classification to apply to a smart trash bin robot. [PERSON] experimented with \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Component** & **Function** \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & Ultrasonic Sensor & Trash Level \\  & Flame Sensor & Flame Alert \\  & Flame Sensor & Live map \\  & GPS & Android Application \\  & & Trash Collection efficiency \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & Wi-Fi Module & Auto-Open/Close Function \\  & Ultrasonic Sensor & Trash Level \\  & RFID reader & Send Message to authority \\  & Infrared Sensor & Provide Reward \\  & LED Screen & Profit from advertising \\ \hline \multirow{4}{*}{[PERSON] [19]} & Ultrasonic Sensor & Trash Level \\  & Bluetooth & Trash Compression \\  & Direct Current (DC) motor & Android Application \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & Digital weight scale & Auto-Open/Close Function \\  & RFID & Provide Reward \\  & Motor & Online Platform \\  & LCD & \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & Ultrasonic Sensor & Trash Level \\  & Force Sensor & Weight Measure of Trash \\  & GPS & Trash collection efficiently \\ \hline \multirow{4}{*}{[PERSON] [22]} & Ultrasonic Sensor & Trash Level \\  & GSM Module & Send Short Message Service (SMS) \\ \cline{1-1}  & RIP Sensor & Play Audio When People Approach \\ \cline{1-1}  & Speaker & Power Bank \\ \cline{1-1}  & Solar Cell Panel & Self Power \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Ultrasonic Sensor & Trash Level \\ \cline{1-1}  & Gas Sensor & Bad Smell Alert \\ \cline{1-1}  & Wi-Fi Module & Android Application \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies Related to Smart Trash Bin Model Design. Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), k Nearest Neighbour (kNN), and CNN models for trash classification tasks, with 65%, 62%, 70%, 52%, and 89% efficient performance, respectively, and showed that CNN performs well on trash classification tasks [25]. [PERSON] et al. [26] proposed RecycleNet for trash classification tasks and conducted comparative experiments with various models such as the Residual Neural Network 50-layer (ResNet50). ResNet50 is also called the Deep Residual Network and has won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). Among them, ResNet50 is the most commonly used model in residual network architectures. RecycleNet is a model that changes the connection pattern of skip connections within dense blocks in the Densely Connected Convolutional Networks (DenseNets) to improve predictive performance. DenseNets is one of the more efficient deep CNN architectures because it contains shorter connections between the layers close to the inputs and outputs, also called the densely connected convolutional networks. RecycleNet and ResNet50 achieved 75% and 81% accuracy in the trash classification task, respectively. [PERSON] et al. proposed a trash classification model, RecycleTrashNet [27]. The proposed model was built on a deep residual network and used composite polling, a new polling method for building network architectures. Deep Neural Networks for Trash Classification (DNN-TC) proposed by [PERSON] et al. are based on Aggregated Residual Transformations for Deep Neural (ResNeXt) [28]. ResNeXt was introduced as Computer Vision and Pattern Recognition (CVPR) in 2017 by [PERSON] et al. [29]. ResNeXt has a new factor called \"cardinality\" (the size of the set of transformations), in addition to the dimensions of depth and width. The authors have shown on various datasets that ResNeXt can be introduced to improve accuracy. [PERSON] et al.