# Classification of Trash and Valuables with Machine Vision in Shared Cars
## 1 Introduction
For the classification of litter in non-typical environments, such as underwater, specialized image datasets have been compiled [5; 6]. Convolutional neural network (CNN) models have been recognized to be a popular choice among researchers to create garbage detection and classification models that is evident in [2; 3; 7; 8; 9]. A frequent approach was to import a predefined neural network model, examples of which include AlexNet and ResNet, followed by the step of training the network's hyperparameters (which include techniques of transfer learning and fine tuning) to ultimately create an application specific prediction model. The CNN trained by [PERSON] [2] produced an accuracy of 22% that, as the research presented, was due to the insufficient requirements of the dataset and lack of model training. However, precise tuning of the hyper-parameters in prediction models, yielded accuracies of up to 87% using a ResNet-34 model [8] and 84% using a max pooling CNN [9]. The classification of waste among the six distinct categories given in [2] was also performed in a recent study using a functional link neural network and yielded an accuracy of 98.61% in a custom dataset created in the study [10]. Furthermore, [PERSON] et al. [5] presented, in their research, the applicability of commonly used existing object detection models, such as YOLO [11], to classify garbage, highlighting their execution speeds using different processors. Vision-based prediction models created with conventional algorithmic approaches for trash detection outdoors were observed as follows: scale-invariant feature transform (SIFT) [2] yielded an accuracy of 63% in its custom image dataset, Viola-Jones detection [12] yielded an accuracy of 82.7% over a total of 1000 images and Karhunen-Loeve (KL) expansion [13] with an unspecified accuracy due to not being implemented in a real time application. Yet, image classification was performed using support vector machine (SVM) across all three applications listed above, despite the difference in the choices of detection techniques. Another observation made in former studies was the development of special purpose interior sensors to perform the task of monitoring the cleanliness status in vehicle cabins. Such installations were evident in [14], in which a camera was used for cabin surveillance and safety mechanism activation, and in [15], in which the basic viability of identifying objects on the seating area of vehicles was experimented with the use of a transducer and electric field sensor combination. Henceforth, it is discernible that the state-of-the-art in algorithmic techniques based on computer vision is well set up. Therefore, it is evident that the core requirement is to not develop the code from scratch but to import the base concepts of a suitable vision-based model that currently exists and alter their framework (as well as the relevant variable combinations) alongside applicable code level expansions that would complement the application of identifying trash and valuables inside shared vehicles. Moreover, the integration of suitable automation to monitor internal cleanliness is still in precursory stages of development. Thus, the research gap was identified to be in the use of a suitable algorithmic technique, based on machine vision, to monitor the interior cleanliness as well as seat occupancy (with respect to leftover items) in shared vehicles when there are no passengers inside. The goal would then essentially be to classify images with trash and valuables in the rear seating area of the vehicle using a suitable vision-based architecture and compare the results with known tagged data to prove the accuracy of the system. We believe our work will ultimately contribute towards the future management and growth of shared vehicle usage, thus, contributing towards the mitigation of energy consumption in transportation. This paper is organized as follows: Section 2 presents the methodology; Section 3 presents the results; Section 4 presents the discussion; and Section 5 presents the conclusion.