# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 1 Introduction  
General waste recycling methods can be classified into curbside recycling and paid recycling [1]. curbside recycling is a simple way to collect recyclable waste, but it cannot reward recycling with payment. On the other hand, reverse vending machine (RVM) [2] is a famous paid recycling method in Europe and the United States, which returns payment for people who recycle resources such as polyethylene terephthalalate (PET) bottles or aluminum scans. This direct compensation for recycling can increase the recycling rate. Based on this advantage, studies on introducing RVM for the smooth collection of recycling resources have been conducted [3, 4, 5]. RVM can also be adopted in smart and sustainable cities for waste management. One of the core elements for waste management in smart and sustainable cities is an on-time collection system with intelligent sensor-based infrastructure and the classification of waste [6]. To meet this goal, studies suggested and surveyed waste management with cloud or Internet of Things (IoT) based systems [7, 8]. By this time, RVM has been a successful solution for waste management in many regions [9], and the system is often configured with the embedded platform. Therefore, RVM can be further developed into asystem suitable for smart and sustainable cities by applying more accurate image-based classification and IoT with a cloud-based system. The essential requirements for the RVM system are the accurate and rapid classification of input resources and detection of fraud objects that intend to fool recognition systems [10; 11]. The RVM systems currently employ various sensors such as barcode sensors, weight sensors, or near-infrared sensors to satisfy these requirements. The barcode sensor-based RVM system requires binary encoding information of the input resources and its vast up-to-date database. Since barcode sensors can only classify the resources with readable binary encoding information, a system based on barcodes may limit the scope of the target resources. The use of a near-infrared sensor enhances the detection rate. However, it leads to high configuration costs of the RVM system, which may impede the active installation and enhanced accessibility of RVM. Convolutional neural network (CNN) is one of the most commonly applied techniques for computer vision. It can broaden the scope of the input resources for the RVM system with the dataset consisting of various objects. Furthermore, CNN can decrease the configuration cost of the RVM system because the vision-based RVM system only requires the camera to classify input resources. Therefore, many studies have been conducted to apply CNNs for the RVM system. For example, [PERSON] et al. [12], [PERSON] and [PERSON] [13] compared the performance of single image-based systems with multiple CNNs to classify input objects. [PERSON] et al. [14] showed that a single image-based system cannot accurately identify intended fraud objects and proposed a multi-modal network that uses various modalities such as image, weight, and ultrasound. Although this multi-modal network system is robust against fraud objects, the cost incurred in the modality measurement and integration is high. In summary, the challenges of the current RVM are the limited scope of input resources, high system configuration cost, and inaccurate classification of intentional fraud objects. To come up with these challenges, we propose a dual image-based CNN ensemble model suitable for a lightweight embedded artificial intelligence (AI) platform. The main contributions of our work are as follows:  
1. We constructed an open access dataset for RVM with three classes by building the prototype RVM and collecting the recycling resources. Since it is challenging to find an adequate open access dataset for RVM, our open access dataset can be one of the baselines for future RVM studies;
2. We also analyzed a limitation of conventional single image-based systems that inaccurately classify fraud objects through extensive experiments.