# Classification of Trash and Valuables with Machine Vision in Shared Cars
## 5 Conclusions  
Our research commenced with the goal of establishing the viability of utilizing an autonomous monitoring and classification system based on computer vision for detecting various types of trash as well as valuables (both categories of which correspond to itemsleft behind by passengers) in the rear seating area of a shared car. Subsequently, a suitable in-car camera unit was implemented and integrated to an existing power configuration (of the research vehicle used in the current study) to capture the state of the seating area. Although the implemented configuration did not correspond to a final solution, the installation was recognized to be compact, affordable and effective for the purpose of capturing an image efficiently upon receiving the command. The communication setup proved successful in transferring a captured image to a remote server in which the prediction model was executed for classification. Future work will focus on a more modular camera unit that will draw power from the 12 V line of the vehicle itself. The use of an infrared (IR) camera will also be experimented to perform detections when there is a lack of daylight and illumination, thereby, eliminating the use of an extra LED light. To classify an image into one of the three distinct classes in the scope of the current study, two algorithmic models were investigated. The primary algorithm was a fine-tuned VGG16 CNN model, which achieved an accuracy of 91.43% on a pre-compiled batch of test images. A set of distinct error sources in the classification outcomes were observed and elaborated. These included errors due to certain forms of shadows, unsuitable levels of ambient light, a lack of item contrast with the neighbouring background and the position of the driver seat. Background subtraction was used as a comparative algorithmic approach; however, in the current study, the application of this method failed as a result of not being able to form a background model. The cause for not being able to generate an applicable estimate of the background was identified to be the presence of high nominal variation (as a result of the varying front seat positions and shadows) present in the empty images. The applicability of using computer vision to classify trash as well as valuables within the cabin of a shared vehicle was observed to function successfully for a single model of a vehicle. Specifics with respect to the distinct patterns of incorrect classifications, the reasoning behind the reduction in accuracy for the classified images and potential methods by which optimization could be performed to address the misclassifications in future studies were provided. To address the current difficulties and prepare the system for real-world use, advanced algorithm development will be conducted with respect to the prediction model. The final aim is to develop a system capable of providing a cleanliness level for different types of vehicle models on a predetermined scale with suitable alerts. Moreover, we aim to expand the dataset of images by obtaining images from actual shared vehicles in operation in order to improve the generalizability of the model (as this was not possible in the current research due to practical difficulties pertaining to the pandemic situation). The current definition of interior cleanliness will also be expanded by integrating other detection criteria, such as dirt, spills, stains and odour, using suitable sensor fusion techniques. Furthermore, a modular camera unit with an inbuilt flash feature, which could be effortlessly installed, will be developed to enable the capability of capturing a larger area of the car interior (including the front seats) as well as to obtain images from a shared vehicle in real-time operation. Conceptualization, N.J., K.K., R.O. and K.T. ; methodology, N.J., K.K. and J.P.; software, N.J.; validation, N.J. and K.K. ; formal analysis, N.J., K.K. and K.T. ; investigation, N.J. and K.K. ; resources, N.J., R.O.