发表机构
School of Information Science and Technology, Beijing University of Technology; College of Computer Science, Beijing University of Technology（北京工业大学信息科学与技术学院; 北京工业大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对铁路异物检测的长尾小样本问题，提出RailGen智能体生成高质量小异物样本，结合FocalDEIM框架优化检测，显著提升了检测性能。
AI 中文摘要
长尾数据分布下的小目标检测是多媒体领域一项基础且具有挑战性的问题，铁路异物检测（RFOD）集中体现了这一挑战——存在易混淆的小型入侵目标且样本稀缺。为解决这些问题，我们提出一种生成增强型检测范式，利用多模态图像生成丰富稀有小目标的特征空间。我们首先构建RailGen，一种基于大模型的多模态图像生成智能体；在语义约束下，RailGen自动调用工具生成铁路场景、校准入侵目标位置、提取异物并将其融合为逼真的入侵效果，该过程生成的高质量合成样本可有效增强尾类的特征表示、完善小目标特征空间。在该范式内，我们进一步提出FocalDEIM，一种利用生成数据增强训练的检测框架；FocalDEIM通过焦点调制改进密集匹配以提升小目标区分度，并采用焦点损失强调难例，从而缓解复杂铁路场景中模糊的类间边界。实验结果表明，RailGen可生成高质量的小规模异物，将目标像素面积分别降低至原来的1/58和平均1/13.85；配备这些具有挑战性的样本后，我们的范式在mAP@50和mAP@(50-95)上分别超越基线DEIM 5.6%和7.5%，且优于现有最先进方法；消融研究验证了RailGen的特征空间增强能力和FocalDEIM的边界区分能力，该范式为安全关键应用中的长尾小目标检测提供了一种有效的多模态生成解决方案。
英文摘要
Small-object detection under long-tailed data distributions is a fundamental yet challenging problem in multimedia. Railway Foreign Object Detection (RFOD) epitomizes this challenge with easily confused small intrusions and scarce samples. To address these issues, we propose a generative-augmented detection paradigm that leverages multimodal image generation to enrich the feature space of rare and small objects. We first construct RailGen, a multimodal image generation agent based on large models. Under semantic constraints, RailGen automatically invokes tools to generate railway scenes, calibrate intrusion positions, extract foreign objects, and fuse them into realistic intrusion effects. This process produces high-quality synthetic samples that effectively densify the feature representations of tail classes and complete the small-object feature space. Within this paradigm, we further propose FocalDEIM, a detection framework designed to enhance training with generated data. FocalDEIM improves dense matching with Focal Modulation for better small-object discrimination and adopts Focal Loss to emphasize hard samples, thereby alleviating blurred inter-class boundaries in complex railway scenes. Experimental results demonstrate that RailGen can generate high-quality small-scale foreign objects, reducing the object pixel area by up to 58x and 13.85x on average. Equipped with these challenging samples, our paradigm surpasses the baseline DEIM by 5.6% and 7.5% in mAP@50 and mAP@(50-95), respectively, and outperforms existing state-of-the-art methods. Ablation studies verify RailGen's feature-space enrichment and FocalDEIM's boundary discrimination. The paradigm provides an effective multimodal generative solution for long-tailed small-object detection in safety-critical applications.
