# Classification of Trash and Valuables with Machine Vision in Shared Cars
## 1 Introduction  
The growth of public transportation along with shared vehicles and driverless taxis has attracted significant attention in modern times in an effort to reduce traffic on roads and vehicle emissions [1]. Thereby, to encourage passengers to use these means of transport over private vehicles, it is pivotal to maintain and ensure elevated levels of cleanliness especially with respect to the interior condition of vehicles subjected to public use on a regular basis. Hence, the project conducted here was focused on detecting distinct types of trash items as well as valuables that would be left behind by users across the seating area of shared passenger vehicles. In the context of the current research, valuables include different types of personal belongings that are common and must be returned to a passenger, while trash corresponds to leftover items that can be directly discarded from the vehicle. The detection was based on the use of machine vision. Upon prompt identification of any such undesirable sources inside the vehicle, the maintenance personnel would be able to take immediate action to eliminate the exposure of the leftover items to the next passengers of the vehicle. Additionally, it would also be possible to produce the identity of passengers who have left their personal belongings in order to return them. Through the timely detection of leftover items, customer confidence in using public transport modes will ultimately be enhanced. Furthermore, in the current research, the possibility of using such a detection system embedded in a shared vehicle was studied by creating a prediction model based on computer vision and analysing its outcomes to prove the viability of the implementation inreal-world use-cases. Subsequently, in the current research, the proposed method is to capture images at scheduled time intervals using an in-car camera unit. The images would correspond to the interior seating area and would be processed autonomously in order to determine the state of cleanliness within the cabin of the vehicle. While this article is concerned with detecting leftover items in the cabin, it should be noted that cleanliness contains other factors too, such as stains in the cabin, the cabin odour and gravel or mud in the cabin (which are not addressed in the scope of the current research). The specific form of detection in the current research corresponds to classification. The prediction model we propose to implement will classify a given image to one out of three possible output classes: Empty (clean), Trash (containing items of trash, such as cans and bottles) and Valuable (containing customer belongings, such as mobiles, wallets, backpacks and key sets). Therefore, in our project scope, the model was not developed to perform object recognition but categorization within three distinct classes. The proposed camera system will be limited to the rear seating area of the vehicle only. The prediction model was expected to produce a minimum classification accuracy of 85% with varying lighting considered (as the illumination levels within the vehicle will be dependent on the time of day and weather conditions). Additionally, a custom annotated dataset will be constructed and put together for the purposes of reuse or optimization in future development work based on similar research scopes. Consequently, the novelty of the current research is achieved through a custom dataset creation and an application-specific algorithmic architecture for prediction using computer vision. Moving on to the state-of-the-art, the image database compiled by [PERSON] [2], which included up to 500 images across six different litter classes, was found to be the most widely used choice for creating trash classification models based on neural networks [2; 3]. Another choice was the VN-trash dataset, which was substantially bigger, with up to 5900 images but across merely three classes of waste categories [4].