Uncertainty-Aware Evaluation of Deep Learning Object Detectors under Scarce and Evolving Test Datasets

Puntos clave

• Evaluation shows enhanced robustness for deep learning object detectors under evolving conditions.
• Key evidence indicates that models maintain a performance level above 80% in challenging datasets.
• Analysis of uncertainty-aware techniques provides insights into model reliability amidst data scarcity.
• Findings highlight potential improvements needed for real-world applications, indicating ongoing challenges.