Towards Deep Continual Workspace Monitoring: Performance Evaluation of CL Strategies for Object Detection in Working Sites

Aslı Çelik¸ Oguzhan Urhan, Andrea Cossu, Vincenzo Lomonaco, Towards Deep Continual Workspace Monitoring: Performance Evaluation of CL Strategies for Object Detection in Working Sites, ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024.

Abstract: Object detection plays a crucial role in computer-based monitoring tasks, where the adaptability of object detection algorithms to complex and dynamic backgrounds is essential for achieving accurate and stable detection performance. Despite the effectiveness of state-of-the-art object detectors, continual object detection remains a significant challenge in real-world applications. In this study, we utilized a dataset tailored for continual object detection in diverse working environments. Using this dataset, a task-incremental and task-agnostic continual learning scenario was established in which each experience, corresponding to object detection sub-datasets collected from different work sites. Common baseline continual learning (CL) strategies were employed throughout the continual training process to evaluate their efficacy. Our findings, consistent with the CL literature, underscore replay-based strategies as the top performers, assessed across both task-aware and task-agnostic settings. Additionally, zero-shot object detection demonstrates notably lower performance compared to the best-performing CL strategies, emphasizing the critical importance of CL strategies in maintaining consistent detection performance and adapting to new environments and work sites.