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 this https URL

本文构建了第一个大规模的多类别可见 - 热红外小目标检测（RGBT SOD）基准数据集（RGBT-Tiny），包括 115 对序列，93K 帧和 1.2M 标注，同时提出了一种适应尺度的健康度量标准（SAFit），并进行了广泛的评估。

可见热微小目标检测：基准数据集和基线模型

Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines

Tracking objects can be a difficult task in computer vision, especially when
faced with challenges such as occlusion, changes in lighting, and motion blur.
Recent advances in deep learning have shown promise in challenging these
conditions. However, most deep learning-based object trackers only use visible
band (RGB) images. Thermal infrared electromagnetic waves (TIR) can provide
additional information about an object, including its temperature, when faced
with challenging conditions. We propose a deep learning-based image tracking
approach that fuses RGB and thermal images (RGBT). The proposed model consists
of two main components: a feature extractor and a tracker. The feature
extractor encodes deep features from both the RGB and the TIR images. The
tracker then uses these features to track the object using an enhanced
attribute-based architecture. We propose a fusion of attribute-specific feature
selection with an aggregation module. The proposed methods are evaluated on the
RGBT234 \cite{LiCLiang2018} and LasHeR \cite{LiLasher2021} datasets, which are
the most widely used RGBT object-tracking datasets in the literature. The
results show that the proposed system outperforms state-of-the-art RGBT object
trackers on these datasets, with a relatively smaller number of parameters.

提出了一种深度学习的图像跟踪方法，将 RGB 图像和红外热像合并进行特征提取和人工属性应用的跟踪，此方法在 RGBT234 和 LasHeR 这两个最广泛使用的 RGBT 目标跟踪数据集上表现优异。