Object detection in fire rescue scenarios is importance for command and decision-making in firefighting operations. However, existing research still suffers from two main limitations. First, current work predominantly focuses on environments such as mountainous or forest areas, while paying insufficient attention to urban rescue scenes, which are more frequent and structurally complex. Second, existing detection systems include a limited number of classes, such as flames and smoke, and lack a comprehensive system covering key targets crucial for command decisions, such as fire trucks and firefighters. To address the above issues, this paper first constructs a new dataset named "FireRescue" for rescue command, which covers multiple rescue scenarios, including urban, mountainous, forest, and water areas, and contains eight key categories such as fire trucks and firefighters, with a total of 15,980 images and 32,000 bounding boxes. Secondly, to tackle the problems of inter-class confusion and missed detection of small targets caused by chaotic scenes, diverse targets, and long-distance shooting, this paper proposes an improved model named FRS-YOLO. On the one hand, the model introduces a plug-and-play multidi-mensional collaborative enhancement attention module, which enhances the discriminative representation of easily confused categories (e.g., fire trucks vs. ordinary trucks) through cross-dimensional feature interaction. On the other hand, it integrates a dynamic feature sampler to strengthen high-response foreground features, thereby mitigating the effects of smoke occlusion and background interference. Experimental results demonstrate that object detection in fire rescue scenarios is highly challenging, and the proposed method effectively improves the detection performance of YOLO series models in this context.

翻译：火灾救援场景中的目标检测对于消防指挥与决策具有重要意义。然而，现有研究仍存在两大局限：首先，当前工作主要集中于山地、森林等环境，对发生频率更高且结构更复杂的城市救援场景关注不足；其次，现有检测系统涵盖类别有限（如火焰、烟雾），缺乏覆盖消防车、消防员等指挥决策关键目标的完整体系。针对上述问题，本文首先构建了面向救援指挥的新数据集"FireRescue"，该数据集覆盖城市、山地、森林、水域等多类救援场景，包含消防车、消防员等8个关键类别，共计15,980张图像与32,000个标注框。其次，为应对场景混乱、目标多样、远距离拍摄导致的类间混淆与小目标漏检问题，本文提出改进模型FRS-YOLO。该模型一方面引入即插即用的多维协同增强注意力模块，通过跨维度特征交互增强易混淆类别（如消防车与普通卡车）的判别性表征；另一方面集成动态特征采样器以强化高响应前景特征，从而缓解烟雾遮挡与背景干扰的影响。实验结果表明，火灾救援场景的目标检测具有高度挑战性，所提方法能有效提升YOLO系列模型在此场景下的检测性能。