Ea-yolo: efficient extraction and aggregation mechanism of YOLO for fire detection

For fire detection, there are characteristics such as variable samples feature morphology, complex background and dense targets, small samples size of dataset and imbalance of categories, which lead to the problems of low accuracy and poor real-time performance of the existing fire detection models. We propose EA-YOLO, a flame and smoke detection model based on efficient multi-scale feature enhancement. In order to improve the extraction capability of the network model for flame smoke targets' features, an efficient attention mechanism is integrated into the backbone network, Multi Channel Attention (MCA), and the number of parameters of the model is reduced by introducing the RepVB module; at the same time, we design a multi-weighted, multidirectional feature neck structure called the Multidirectional Feature Pyramid Network (MDFPN), to enhance the model's flame smoke target feature information fusion ability; finally, we redesign the CIoU loss function by introducing the Slide weighting function to improve the imbalance between difficult and easy samples. Additionally, to address the issue of small sample sizes in fire datasets, we establish two new fire datasets: Fire-smoke and Ro-fire-smoke. The latter includes a model robustness validation function. The experimental results show that the method of this paper is 6.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 7.3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} higher compared to the baseline model YOLOv7 on the Fire-smoke and Ro-fire-smoke datasets, respectively. The detection speed is 74.6 frames per second. To fully demonstrate the superiority of EA-YOLO, we utilized the public FASDD dataset and compared several state-of-the-art (SOTA) models with EA-YOLO on this dataset. The results were highly favorable. It fully demonstrates that the method in this paper has high fire detection accuracy while considering the real-time nature of the model. The source code and datasets are located at https://github.com/DIADH/DIADH.YOLO.