Transfer Learning Approach Towards a Smarter Recycling

The increasing consumption of electrical and electronic devices is alarming. Therefore, the transition from linear to circular economy becomes essential. The key solution to support this transformation is artificial intelligence. This work presents a transfer learning approach to support the recycling of electrical and electronic waste (Ewaste). We emphasize the use of transfer learning technique, particularly, to classify Ewaste. In this approach, we design a hybrid model of residual nets and inception modules that can classify features of a source domain (smartphones in our case) and leverage this knowledge to another device (electric screwdrivers, as an example). Using our model, we achieve an overall accuracy of 94.27% and 97.22%, respectively. These are comparable to the popular pre-trained models, which use similar network topologies. We use a web crawler program for collecting images from search engines to build the datasets with less efforts. We show that transfer learning is more robust and performs better than training from scratch. It avoids duplication and waste of computational resources. As a result, with the benefits of transfer learning, we can provide detailed information about the devices that need to be recycled. Ultimately, this would greatly support the overall recycling process.