Mirror Target YOLO: An Improved YOLOv8 Method with Indirect Vision for Heritage Buildings Fire Detection

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 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.