Object detection models and active learning for improvement of e-waste collection management systems in Korea

This study identifies and analyzes issues within the management system of the waste home appliances free pickup service and seeks to enhance the system by using an object detection model. To overcome the limitations of manually inspecting approximately 5,000 collections per day, the YOLOv8 model was implemented. Photos for proof of collection, which were difficult to verify visually, were excluded from the image data. Labeling was performed on items defined by waste throughput, resulting in a dataset of 19,101 images. The initial training model achieved performance metrics of 0.950 mAP50 and 0.888 mAP50-95. The detection process for 11,003 images, including saving a summary file, took 7 min and 32.8 s. This method allows the system to automatically identify discrepancies between collection managers' registered data and the actual items collected. Additionally, active learning methods are proposed as a future enhancement strategy for the model. To improve future model performance, some data samples could be selected based on uncertainty by assigning weights to address class imbalance. The model generates bounding boxes for its predictions, and human annotators can verify these results, thereby reducing the cost of manual labeling. This approach can contribute to improving learning efficiency for any new data added later. Experimental results demonstrate that this method effectively resolves class imbalance issues and improves model performance through uncertainty sampling. It is expected that this approach will improve the existing manual inspection systems and maximize the efficiency of the future management process.