ABSTRACT
The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material. One important step in the overall recycling process is the evaluation and the value estimation for further treatment of the waste PCBs (WPCBs). In this article, we introduce a method for value estimation of entire WPCBs based on component detection. The value of the WPCB is then predicted by the value of the detected components. This approach allows a flexible application to different situations. In the first step, we created a dataset and labelled the components of 104 WPCBs using different component classes. The component detection is performed on dual energy X-ray images by the deep neural object detection network 'YOLO v5'. The dataset is split into a training, validation and test subset and standard performance measures as precision, recall and F1-score of the component detection are evaluated. Representative samples from all component classes were selected and analysed for the valuable materials to provide the ground truth of the value estimation in the subsequent step.
ABSTRACT
Firefighters, paramedics, nursing staff, and other occupational groups are in constant need of fast and proper cleaning of their professional workwear, not only during a pandemic. Thus, laundry technology needs to become more efficient and automated. Unfortunately, some steps of the cleaning process, such as finding and removing foreign items from pockets or belts, are still completed manually. This is not just time-consuming but potentially dangerous for the workers due to the hazardous nature of items such as scissors, scalpels, or syringes. Additionally, some items may damage the garments by staining or harm the laundry machines, causing malfunctions and process failure. On the one hand, these foreign items are often hidden inside the clothes, making detection very challenging with conventional superficial sensors. On the other hand, these items can be diverse and cannot be detected by metal detectors alone. X-ray transmission has proven to be a powerful tool for detecting items inside of objects. The dual-energy approach (DE-XRT) even allows obtaining quantitative information about the chemical composition of the measured materials. In this study, working garments were accompanied and filled with realistic foreign items. The potential of DE-XRT to detect those items was successfully shown.
