Exploring deep learning for sorting shredded end-of-life wind turbine blades

The increasing popularity of wind energy has led to an extensive growth in wind turbine installations. This poses challenges when these wind turbine blades reach the end of their operational lifespan. Thus, suitable recycling techniques are required to extract the different materials from the turbine blades. Therefore, we explored the use of deep learning-based object detection for sorting shredded End-of-Life wind turbine blades into individual material classes. To reach this goal, a custom dataset combining images from shredded wind turbine blades, with a synthetic data generation approach, was employed. Three popular object detection architectures (SSD, YOLO, and Faster RCNN) were implemented and tested. The impact of different popular backbone networks and Feature Pyramid Networks on accuracy and speed was analyzed. Our results showed that the SSD model with a ResNet18 backbone and a version of Feature Pyramid Network performed well in terms of accuracy and speed. Moreover, synthetic data generation proved useful but showed a performance decline when transitioning to real-world pictures, suggesting the need for combining synthetic and real-world data. This study highlights the potential of deep learning in recycling and sorting wind turbine blades, encouraging further research in this field.