Continual Learning in an Industrial Scenario: Equipment Classification on Edge Devices

The ability to incrementally learn to categorize objects is a key feature for a personalized system in real-world applications. The major constraint for such scenario relies on the catastrophic forgetting problem, which negatively impacts the performance of the models on previously learned representations. In this work, we developed an equipment classification model to be deployed on edge devices by applying regularization and memory-based class-incremental strategies, such that it can detect new classes while preserving its ability to detect previously known classes, mitigating the forgetting phenomenon. The strategies were tested on three datasets: CIFAR100 to validate the implementation, Stanford Dogs to ensure the reliability of the results as it is a more representative dataset, and SINATRA, which is the work's industrial dataset for equipment recognition. Experimental results on these datasets show that the Experience Replay strategy performed better. For the SINATRA dataset, average accuracy values of 95.57% and of 100% were achieved for Águas e Energias do Porto and Plastaze subsets, respectively. The outcomes of this work proved that by retaining only a limited number of exemplars from old classes, it is possible to update a pre-existing system to classify new devices in a shorter period and avoid catastrophic forgetting.