Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection
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 pr...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836736/ |
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| author | Jian Liang Junsheng Cheng |
| author_facet | Jian Liang Junsheng Cheng |
| author_sort | Jian Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-21582ee8a816454594bf17fb9b3d48ec |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-21582ee8a816454594bf17fb9b3d48ec2025-01-24T00:01:57ZengIEEEIEEE Access2169-35362025-01-0113111951120310.1109/ACCESS.2025.352804610836736Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire DetectionJian Liang0https://orcid.org/0009-0001-5734-6139Junsheng Cheng1https://orcid.org/0000-0003-0135-5340College of Mechanical and Vehicle Engineering, Hunan University, Changsha, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, ChinaFires 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.https://ieeexplore.ieee.org/document/10836736/Fire detectionindirect visionmaskYOLOv8 |
| spellingShingle | Jian Liang Junsheng Cheng Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection IEEE Access Fire detection indirect vision mask YOLOv8 |
| title | Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection |
| title_full | Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection |
| title_fullStr | Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection |
| title_full_unstemmed | Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection |
| title_short | Mirror Target YOLO: An Improved YOLOv8 Method With Indirect Vision for Heritage Buildings Fire Detection |
| title_sort | mirror target yolo an improved yolov8 method with indirect vision for heritage buildings fire detection |
| topic | Fire detection indirect vision mask YOLOv8 |
| url | https://ieeexplore.ieee.org/document/10836736/ |
| work_keys_str_mv | AT jianliang mirrortargetyoloanimprovedyolov8methodwithindirectvisionforheritagebuildingsfiredetection AT junshengcheng mirrortargetyoloanimprovedyolov8methodwithindirectvisionforheritagebuildingsfiredetection |