Multi-Scale Construction Site Fire Detection Algorithm with Integrated Attention Mechanism - Haipeng Sun; Tao Yao (Fire)
The occurrence of construction site fires is consistently accompanied by casualties and property damage. To address the issues of large target-scale variations and frequent false detections in construction site fire monitoring, we propose a fire detection algorithm based on an improved YOLOv8 model, achieving real-time and efficient detection of fires on construction sites. First, considering the wide range of scale variations in detected objects, an additional detection layer with a 64-times down-sampling rate is introduced to enhance the algorithm’s detection capability for multi-scale targets. Then, the MBConv module and the ESE attention block are integrated into the C2f structure to enhance feature extraction capabilities while reducing computational complexity. An iCBAM attention module is designed to suppress background noise interference and enhance the representation capability of the network. Finally, the WIoUv3 metric is adopted in the loss function for bounding box regression to mitigate harmful gradient issues. Comparative experiments demonstrate that, on a self-constructed construction site fire dataset, the improved algorithm achieves an accuracy and recall increase of 4.6% and 3.0%, respectively, compared to the original YOLOv8 model. Additionally, mAP50 and mAP50-95 are improved by 1.6% and 1.5%, respectively. This algorithm provides a more effective solution for fire monitoring in construction environments.