Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data

Increasing environmental awareness and new regulations require an improvement of the waste cycle of plastic packaging. Tracer-Based-Sorting (TBS) technology can meet these challenges. Previous studies show the market potential of the technology. This work improves on the solution approach using artificial intelligence to maximize the number of tracers that can be detected accurately. A convolutional neural network and random forest classifier are compared for classification of each tracer based on signal intensities. The approach is validated on different settings using synthetic data to counter the low amount of available data. The results show that theoretically up to 120 tracers can be classified simultaneously under near-optimal conditions. Under more difficult conditions, the maximum number of tracers is reduced to 45. Thus, the approach can increase the diversity of TBS by increasing the maximum tracer count and enable a broader range of applications. This helps to establish the technology in the field of recycling.