# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
## 1 Introduction  
Waste electrical and electronic equipment (WEEE) management is a concern in many countries [1] since electrical and electronic equipment production has increased while the average lifetime of these products has decreased [2]. Consequently, WEEE has the fastest growth rate in the industrialized world [3]. This concern has led to the development of legislation and directives to manage this sort of waste, for example, the European Directive for WEEE and the Brazilian Policy of Solid Waste (BPSW) [4]. The European Commission recently adopted Implementing Regulation (EU) 2019/290, which focuses on restricting the use of hazardous substances in electrical and electronic equipment in the WEEE Directive of 2003. The evolution of laws and regulations demonstrates an advancement regarding the impacts that the improper disposal of electronic materials can have on the environment, thus the development of projects in this line of research positively reflects the industry's current sustainability issues. Printed circuit boards (PCB) are an essential element present in almost all electrical and electronic equipment [2]. The PCB is the platform that connects the discrete andintegrated electronic components. Recycling waste printed circuit boards (WPCBs) can recover valuable materials (metallic and nonmetallic), besides the possibility of the reuse of some components [5]. WPCBs are the major source of metals in electronic waste [6]. Even given the high-value materials, WPCB recycling is difficult because of the diversity in their compositions [7; 8]. Dismantling and separating of the WPCB components is very important to achieve the maximum recycling outcomes. However, completely automating this pre-processing is a high-cost process, and it is performed manually in primitive methods in some countries [9]. In order to optimize the recycling WPCB process, vision systems can be used to input data to desoldering systems and identify reusable and hazardous components [7; 10]. Aiming to promote computer-vision-based strategies for PCB recycling, the PCB DSLR public dataset was published [10]. DSLR stands for Digital Single-Lens Reflex camera, and it is a reference of the camera type used to acquire the PCB images. The PCB DSLR Dataset contains high-resolution WPCB images with bounding box annotations for integrated circuits (ICs). Since some ICs may contain valuable materials, it is essential to detect such components in WPCBs. Computer vision systems are a feasible way to perform WPCB evaluation to guide automatic dismantling and recycling [11]. The computer vision task performed is named object detection. Since sufficiently large fully-annotated datasets for the PCB domain are expensive and unavailable [12], the use of state-of-the-art object detectors (based on deep learning) can be impaired because of the dataset size [13]. This sort of problem is known as few-shot learning [14]. The main goal is to learn proper feature embedding that allows the model to be trained with fewer images than standard models. Since ICs are a significant source of highly valuable metals, we propose the WPCB economic feasibility assessment (WPCB-EFA) method for IC recycling from a WPCB by estimating recoverable metals and gross earnings using computer vision to detect ICs in WPCBs. As WPCBs have a high composition diversity, they can be grouped according to their value. For instance, mobile phones and mainframe PCBs have a high value (8 to 25 euros per kg) while calculators, audio scraps, and power supplies are low-value PCBs (less than 1 euro per kg) [15]. The proposed WPCB-EFA can be used to evaluate the composition of a WPCB and select the appropriated recycling process focused on recovering precious metals for high-value PCBs or the nonmetal fraction for low-value PCBs. To evaluate the proposed WPCB-EFA, we used transfer learning of the state-of-the-art YOLOv3 model [16] in a few-shot scenario for IC detection on the PCB DSLR Dataset.