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/XinyiYing/RGBT-Tiny.

翻译：小目标检测（SOD）是数十年来一项长期存在且具有挑战性的任务，已有大量数据集和算法被开发。然而，它们主要关注可见光或热红外单一模态，而可见光-热红外（RGBT）双模态却鲜有探索。尽管近期已开发出一些RGBT数据集，但其数量不足、类别有限、图像未对齐以及目标尺寸过大等问题，无法为评估多类别可见光-热红外小目标检测（RGBT SOD）算法提供公正的基准。本文构建了首个大规模、高多样性的RGBT SOD基准数据集（命名为RGBT-Tiny），包含115组配对序列、9.3万帧图像和120万个人工标注。RGBT-Tiny涵盖丰富目标（7个类别）和高多样性场景（8种类型，覆盖不同光照和密度变化）。值得注意的是，超过81%的目标尺寸小于16x16像素，我们提供了带有跟踪ID的配对边界框标注，从而为RGBT融合、检测与跟踪等广泛应用提供了一个极具挑战性的基准。此外，我们提出了一种尺度自适应拟合度（SAFit）度量方法，其对小目标和大型目标均表现出高鲁棒性。所提出的SAFit能够提供合理的性能评估并提升检测性能。基于提出的RGBT-Tiny数据集和SAFit度量，我们进行了广泛评估，涵盖23种最新的先进算法，这些算法涉及四种不同类型（即可见光通用检测、可见光SOD、热红外SOD和RGBT目标检测）。项目地址：https://github.com/XinyiYing/RGBT-Tiny。