Study on the real-time object detection approach for end-of-life battery-powered electronics in the waste of...

With the growing use of electrical and electronic equipment (EEE), managing end-of-life EEE has become critical. Thus, the demand for sorting and detaching batteries from EEE in real time has increased. In this study, we investigated real-time object detection for sorting EEE, which using batteries, among numerous EEEs. To select products with batteries that have been mainly recycled, we crowd-sourced and gathered about 23,000 image datasets of the EEE with battery. Two learning techniques-data augmentation and transfer learning-were applied to resolve the limitations of the real-world data. We conducted YOLOv4-based experiments on the backbone and the resolution. Moreover, we defined this task as a binary classification problem; therefore, we recalculated the average precision (AP) scores from the network through postprocessing. We achieved battery-powered EEE detection scores of 90.1% and 84.5% at AP scores of 0.50 and 0.50-0.95, respectively. The results showed that this approach can provide practical and accurate information in the real world, hence encouraging the use of deep learning in the pre-sorting stage of the battery-powered EEE recycling industry.