To address the challenges in differentiating between similar scrap classes like Low-Quality Oxyfuel Cutting and Sheared Scrap, exploring additional techniques and model architectures can be beneficial. One approach could involve incorporating ensemble learning, where multiple models are trained and their predictions are combined to make a final classification decision. This can help capture diverse perspectives and improve the overall accuracy of the classification system.
Moreover, exploring advanced feature extraction techniques, such as attention mechanisms or graph neural networks, can enhance the models' ability to capture subtle differences between similar classes. These techniques can focus on specific regions or relationships within the images that are crucial for distinguishing between classes.
Additionally, experimenting with more complex deep learning architectures, such as transformer-based models or graph convolutional networks, can offer a deeper understanding of the intricate patterns present in the scrap material images. These architectures have shown promise in capturing complex relationships and features in various domains and could be beneficial in improving the classification accuracy for challenging classes.