## 1 Introduction  
In recent years, the phenomenon of population aging has become more and more apparent all over the world as the society develops. The aging of the population means a larger proportion of the population aged over 60 years, and also indicates a decrease in the percentage of the working population. In this situation, robots that can help humans to do some work will effectively alleviate the problem of labor shortage. This paper proposes the detection system for empty-dish recycling robots to automatically sort and recycle used dishes for the subsequent process. It has wide application scenarios, including restaurants, cafeterias, and other places with many people. The key for the proposal is the detection of the dishes. In fact, there are many different types of dishes for each scenario as shown in Figure 1, including bowls, plates, cups of different colors and sizes, and even chopsticks and spoons. It is hard work to correctly detect and identify these different types of dishes for recycling. At the same time, in order to apply the empty-dish recycling robot in real life, the time for detection should not be too long, otherwise it would seriously affect the subsequent recycling work. Therefore, our aim is to increase the speed of the detection process as much as possible while ensuring the accuracy of the detection. For the detection of dishes, a deep learning based technique is adopted. Nowadays, the technique has been widely used in various fields of our life, including automatic driving [1], environmental protection [2], cultural heritage protection [3,4], etc. In addition to images, deep learning can also be applied to speech recognition [5], machine translation [6], vulnerability detection [7], and other fields [8,9]. In terms of computer vision, the tasks could be simply classified into two purposes: image classification and object detection. Image classification is mainly to associate one or more recognized labels with the given image. As for object detection, it is to detect the instances of semantic objects in images or videos. The task requires the neural network to recognize various sub-images and locations for drawing bounding boxes around each recognized sub-image. In terms of the hardware platforms for a deep neural network (DNN), although image classification and object detection could be performed extremely fast using a graphics processing unit (GPU), the cost of using GPU is high, it is difficult for GPU to be applied to edge. Thus, we consider deploying a field programmable gate array (FPGA) to dish recycling robots. Compared with GPU, FPGA has the advantages of small size, high portability, and low energy consumption. In addition, FPGA has similar parallel computing features to GPU, which makes the performance of FPGA much better than a central processing unit (CPU). Therefore, in this research, we implement the dish detection system on FPGA. The three major contributions of this paper are as follows:  
* We build a unique object detection dataset of 16 common kinds of dishes, which can be used in the application of empty-dish recycling robots. * We propose a formalized process of deploying a YOLOv3 object detection model on the FPGA platform, which is divided into two parts: software process and hardware process. * We implement a method to reduce the size of the YOLOv3 model through pruning it on the software platform, and enable the pruned model to be deployed to the FPGA evaluation board via Vitis AI, which realizes acceleration of deep learning on the FPGA platform. The rest of this paper is organized as follows: Section 2 introduces the research related to an FPGA-based dish detection system. Section 3 shows the robotics and dish dataset used in this research. Section 4 describes the formalized process of deploying a YOLOv3 object detection model on the FPGA platform, which is divided into software process and hardware process. Section 5 lists specific research results on both software and hardware platforms.