用镜面反射视角提升古建筑火灾检测，减少摄像头数量。
Mirror Target YOLO: An Improved YOLOv8 Method with Indirect Vision for Heritage Buildings Fire Detection
- 通过镜面角度实现间接视觉，解决空间遮挡问题。
- 在800张图像数据集上检测精度优于主流模型。
- 保留管理人员经验，降低误报率，适合古建保护场景。
火灾会对古建筑造成严重损害，及时检测至关重要。传统布线和钻孔方式可能损伤建筑结构，因此减少摄像头数量以降低影响极具挑战性。同时，避免因噪声敏感导致的误报，并保留管理人员对高风险区域的判断经验也极为重要。为此，我们提出一种基于间接视觉的火灾检测方法——镜面目标YOLO（MITA-YOLO）。该方法结合间接视觉部署与增强检测模块，利用镜面角度实现间接视图，解决不规则空间中视野受限的问题，并将每个间接视图精准对齐目标监测区域。目标掩码（Target-Mask）模块可自动识别并隔离图像中的间接视觉区域，过滤非目标区域，使模型继承管理人员对火灾风险区的判断能力，从而提升检测专注度与抗干扰性能。实验中，我们构建了一个包含800张图像的间接视觉火灾数据集。结果表明，MITA-YOLO在显著减少摄像头需求的同时，相比其他主流模型实现了更优的检测性能。
原文摘要 · Abstract (English)
Fires can cause severe damage to heritage buildings, making timely fire detection essential. Traditional dense cabling and drilling can harm these structures, so reducing the number of cameras to minimize such impact is challenging. Additionally, avoiding false alarms due to noise sensitivity and preserving the expertise of managers in fire-prone areas is crucial. To address these needs, we propose a fire detection method based on indirect vision, called Mirror Target YOLO (MITA-YOLO). MITA-YOLO integrates indirect vision deployment and an enhanced detection module. It uses mirror angles to achieve indirect views, solving issues with limited visibility in irregular spaces and aligning each indirect view with the target monitoring area. The Target-Mask module is designed to automatically identify and isolate the indirect vision areas in each image, filtering out non-target areas. This enables the model to inherit managers' expertise in assessing fire-risk zones, improving focus and resistance to interference in fire detection.In our experiments, we created an 800-image fire dataset with indirect vision. Results show that MITA-YOLO significantly reduces camera requirements while achieving superior detection performance compared to other mainstream models.
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