Research on Enhancing Fire Detection Performance in Ancient Architecture Under Occlusion Scenarios Based on YOLO-AR

TL;DR

This paper introduces YOLO-AR, a new fire detection algorithm designed to improve performance in complex ancient architecture settings with occlusions.

Contribution

The novel YOLO-AR algorithm integrates CBAM and Repulsion Loss to enhance fire detection in occluded ancient architectural environments.

Findings

YOLO-AR achieved 90.7% detection precision on a custom ancient architecture dataset.

The algorithm improved recall to 89.7%, outperforming mainstream methods in occluded scenarios.

Experiments showed superior performance in Precision, Recall, and mAP metrics.

Abstract

Fire detection in ancient architecture presents significant challenges due to complex scenes and unique structural characteristics. Traditional detection methods often demonstrate limitations when addressing the specific structural idiosyncrasies of individual ancient buildings and the overlapping occlusion prevalent in architectural complexes. This paper proposes YOLO-AR, a novel fire detection algorithm based on an improved YOLOv8 framework. By embedding the Convolutional Block Attention Module (CBAM) at the end of the backbone network, the algorithm enhances its capability to capture key features of flames and smoke. Furthermore, the Repulsion Loss function is introduced to explicitly optimize bounding box localization accuracy in occluded and dense scenarios. Experiments conducted on a self-constructed ancient architecture dataset comprising 15,847 images demonstrate that YOLO-AR outperforms mainstream comparative algorithms in terms of Precision, Recall, and mean Average Precision (mAP). Specifically, the detection precision reached 90.7%, and the recall rate improved to 89.7%. This study provides an efficient and reliable visual detection solution for early warning systems in ancient architecture, offering significant value for cultural heritage preservation.

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