Robust Thermal Image Object Detection Challenge: Advancing Multi-object Detection Performance under Long-Term Thermal Drift

This paper reviews the Robust Thermal-Image Object Detection (RTIOD) Challenge, organized as part of the 6th Real-World Surveillance (RWS) Workshop at the Winter Conference on Applications of Computer Vision (WACV) 2026. The challenge aims to stimulate the development and rigorous evaluation of object detectors that remain reliable under long-term appearance drift in real-world surveillance settings. To this end, RTIOD is built on the Long term Thermal Drift v2 dataset and evaluates submissions using both global and month-wise metrics (e.g., mAP@0.5), enabling direct assessment of temporal stability over extended periods. In total, 60 participants registered and 14 teams submitted predictions for the final test phase. Be yond reporting the leaderboard and summarizing competing approaches, this paper provides an analysis of top methods succeed. We distill recurring design patterns (e.g., thermal representation choices, data curation strategies, and deployment-informed priors), characterize monthdependent failure modes that reveal seasonal drift effects, and examine dominant error sources (notably localization and classification) that limit performance. The resulting insights offer practical guidance for building more temporally consistent thermal detectors and establish reference baselines and benchmarks for future research in robust thermal image object detection.

Recommended citation: Anders Skaarup Johansen, Marco Parola, Andreas Aakerberg, Mario Cimino, Sergio Escalera, Thomas B Moeslund, Kamal Nasrollahi (2026). "Robust Thermal Image Object Detection Challenge: Advancing Multi-object Detection Performance under Long-Term Thermal Drift", The IEEE/CVF Winter Conference on Applications of Computer Vision 2026: Real World Surveillance: Applications and Challenges, 6th
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