  Small object detection (SOD) has been a longstanding yet challenging task for
decades, with numerous datasets and algorithms being developed. However, they
mainly focus on either visible or thermal modality, while visible-thermal
(RGBT) bimodality is rarely explored. Although some RGBT datasets have been
developed recently, the insufficient quantity, limited category, misaligned
images and large target size cannot provide an impartial benchmark to evaluate
multi-category visible-thermal small object detection (RGBT SOD) algorithms. In
this paper, we build the first large-scale benchmark with high diversity for
RGBT SOD (namely RGBT-Tiny), including 115 paired sequences, 93K frames and
1.2M manual annotations. RGBT-Tiny contains abundant targets (7 categories) and
high-diversity scenes (8 types that cover different illumination and density
variations). Note that, over 81% of targets are smaller than 16x16, and we
provide paired bounding box annotations with tracking ID to offer an extremely
challenging benchmark with wide-range applications, such as RGBT fusion,
detection and tracking. In addition, we propose a scale adaptive fitness
(SAFit) measure that exhibits high robustness on both small and large targets.
The proposed SAFit can provide reasonable performance evaluation and promote
detection performance. Based on the proposed RGBT-Tiny dataset and SAFit
measure, extensive evaluations have been conducted, including 23 recent
state-of-the-art algorithms that cover four different types (i.e., visible
generic detection, visible SOD, thermal SOD and RGBT object detection). Project
is available at https://github.com/XinyiYing24/RGBT-Tiny.
