Mirror Target YOLO: An Improved YOLOv8 Method with Indirect Vision for Heritage Buildings Fire Detection
Jian Liang, JunSheng Cheng

TL;DR
This paper introduces MITA-YOLO, a novel fire detection method for heritage buildings that employs indirect vision via mirrors, reducing camera needs and improving detection accuracy while minimizing structural impact.
Contribution
MITA-YOLO integrates indirect vision with an enhanced detection module, enabling effective fire detection in heritage buildings with fewer cameras and higher accuracy.
Findings
Reduces camera requirements significantly.
Achieves superior fire detection performance.
Effectively filters non-target areas in images.
Abstract
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…
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Taxonomy
TopicsFire Detection and Safety Systems · Video Surveillance and Tracking Methods · Advanced Neural Network Applications
MethodsFocus
