# Classification of Trash and Valuables with Machine Vision in Shared Cars
## 4 Discussion
### Comparison to Previous Studies  
While most of the research work covered in the state-of-the-art chapter focused on the identification and classification of various types of trash, the study presented here emphasized the classification of an image based on unknown objects under the categories of trash as well as valuables in an application specific environment. In this approach, knowing the exact type of trash or valuable is not very useful since the intended response upon classification could be developed with the three classes considered (removal of any identified sources of trash or valuables would always require human intervention as automating this process is not practical). However, we determined that it was useful to know if the image contains valuables or trash (as opposed to binary classification between an empty image and an image containing leftover items) as the maintenance personnel could use the input to determine if the items have to be returned to the passenger. In contrast to most previous neural network-based prediction models [3; 8; 9], the fine-tuned VGG16 used in this study had a simpler architecture and a fewer number of layers (depth). As a result, tuning the model until it reached an acceptable prediction accuracy was made possible. This can be seen in the comparison of the accuracy of our study, which was 91.43%, to earlier models, which had lowest and highest accuracies of 22% and 98%, respectively. The comparison of the accuracies for related classification models is summarized in Table 3. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Process** & **Min Time** & **Max Time** \\ \hline Capturing an image & 0.46 s & 0.98 s \\ Transmission of the captured image (with SCP) & 0.20 s & 4.20 s \\ Prediction model execution & 5.00 s & 12.0 s \\ Total elapsed & 5.66 s & 17.2 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cycle times corresponding to the primary process blocks with respect to the detection system. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Study** & **Model Architecture** & **Accuracy** \\ \hline [PERSON] and [PERSON] [2] & Artificial CNN & 22\% \\ [PERSON] and [PERSON] [3] & ResNet-50 & 87\% \\ [PERSON] et al. [8] & ResNet-34 & 87.26\% \\ [PERSON], [PERSON] and [PERSON] [10] & Functional link neural network & 98.61\% \\ [PERSON] and [PERSON] [2] & SIFT and SVM & 63\% \\ [PERSON] et al. [12] & [PERSON] and [PERSON] & 82.7\% \\ Current research & Fine-tuned Vgg16 & 91.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the classification accuracies of the trash classification models from the previous studies. Note however that each research utilized its own dataset, which adds an element of bias to the comparison. In comparing the classification accuracies given above in Table 3, we were able to determine that the performance primarily depended upon three factors: the network architecture of the prediction model, the extent of hyperparameter tuning and the quantity and variance of the dataset. The functional link neural network [10] is comparatively the most recent research conducted in 2022. It was focused explicitly on garbage management to identify different classes of waste (which was the main focus of other previous work as well except ours). The authors here had built upon the VGG16 architecture with the inclusion of multiple sub modules specifically formulated for the application of garbage classification. They had used a comparatively larger dataset for training (up to 2500 raw images) and were highly precise and elaborate during the hyperparameter tuning and validation phases. Consequently, we are able to observe the highest comparative accuracy of classification for the research in [10] as presented in Table 3. On the other hand, the research in [2] had the lowest classification accuracy. Their training dataset was much smaller (approximately 500 raw images), and the authors of the paper clearly mentioned the lack of training in their CNN classification model.