# Classification of Trash and Valuables with Machine Vision in Shared Cars
## 4 Discussion  
### Evaluation of the Prediction Model  
The algorithmic approaches used in the study yielded opposing findings, with the CNN showing potential and the background subtraction method failing. Consequently, the fine-tuned VGG16 model was in focus when evaluating the results. The outcomes from the CNN revealed that the images responded positively to changes in the rear door states (opened, closed or ajar) across all three classes. This observation is evident in the images in Figure 9, in which there are no trends in certainty based on whether the rear doors are closed, opened or in the ajar state. As a result, this external form of variation in the images had no substantial impact on the certainties.  
When the seat of the driver was moved its rearmost position with respect to how much it can be retracted, as shown in Figure 11, the prediction model was observed to produce anomalous classifications. Note the area circled in red, corresponding to the base of the headset of the seat of the driver, which is only visible when the seat is retracted all the way back to the position shown in the image. The problem was that there was a lack of training images of the seat of the driver in this position.  
Limited contrast and excessive shadows, both of which hamper the potential of the camera to capture the item in a clear and visible manner, were also found to be significant sources of error in the final classification. Figure 11 shows how an instance of an incorrect classification, which can potentially occur in a predicted image when an item having a noticeable amount of glare (in this case, a mobile phone with the display face up) is positioned in an exceptionally bright location of the seat. Figure 11 presents a misclassification of an object that is positioned in a dark area and hence lacks contrast with its neighbouring pixels.  
Figure 11: Samples of misclassified images with red circles corresponding to (**a**) base of the driver’s head rest, (**b**) item subjected to glare, (**c**) item lacking contrast with the neighbourhood and (**d**) item with lack of contrast at one of the furthest points from the camera.  
The use of a camera with higher quality and the use of contrast enhancement filters are two possible remedies for this problem that will additionally be tested in future development work. When objects with a limited contrast are positioned at one of the distant positions with respect to the camera, minor misclassifications between the trash and valuable classes were observed as evident in Figure 11d. Expanding the image dataset, used for training the model, with more quantities of similar cases is one method of correction to address this shortcoming.  
Table 2 presents the cycle times of the distinct sequences of processing within the developed architecture, ranging from capturing an image with the in-car camera unit to classifying the image by the prediction model located in the remote server after successful image transmission. The figures in the table were gathered during the testing phase, and the total cycle time per image was influenced by the network connection strength in most cases. Therefore, if the data were acquired for distinct network connections at various locations, the overall time range for the cycle was expected to be greater than depicted here.