To extend the proposed approach to incorporate real-time data from industrial settings for improving the reliability and adaptability of the ferrous scrap classification system, several key steps can be taken. Firstly, implementing a robust data collection and preprocessing pipeline that can handle streaming data from sensors or cameras in industrial settings is essential. This pipeline should be able to process and format the incoming data for compatibility with the deep learning models used for classification.
Secondly, integrating edge computing capabilities can enable the processing of data closer to the source, reducing latency and enabling real-time decision-making. This can involve deploying lightweight versions of the classification models on edge devices to analyze data as it is generated.
Furthermore, incorporating feedback loops into the system can help continuously update and refine the models based on real-time data and feedback from the classification results. This adaptive learning approach can enhance the system's accuracy and adaptability over time.
Lastly, leveraging techniques such as transfer learning and online learning can facilitate the continuous improvement of the classification models as new data streams in. By updating the models with new information and patterns from real-time data, the system can stay relevant and effective in dynamic industrial environments.