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EFA-YOLO: An Efficient Feature Attention Model for Fire and Flame Detection

Weichao Pan, Xu Wang, Wenqing Huan

TL;DR

An efficient and lightweight flame detection model, EFA-YOLO (Efficient Feature Attention YOLO), which exhibits a significant enhancement in detection accuracy (mAP) and inference speed, with model parameter amount reduced by 94.6 and the inference speed improved by 88 times.

Abstract

As a natural disaster with high suddenness and great destructiveness, fire has long posed a major threat to human society and ecological environment. In recent years, with the rapid development of smart city and Internet of Things (IoT) technologies, fire detection systems based on deep learning have gradually become a key means to cope with fire hazards. However, existing fire detection models still have many challenges in terms of detection accuracy and real-time performance in complex contexts. To address these issues, we propose two key modules: EAConv (Efficient Attention Convolution) and EADown (Efficient Attention Downsampling). The EAConv module significantly improves the feature extraction efficiency by combining an efficient attention mechanism with depth-separable convolution, while the EADown module enhances the accuracy and efficiency of feature downsampling by utilizing spatial and channel attention mechanisms in combination with pooling operations. Based on these two modules, we design an efficient and lightweight flame detection model, EFA-YOLO (Efficient Feature Attention YOLO). Experimental results show that EFA-YOLO has a model parameter quantity of only 1.4M, GFLOPs of 4.6, and the inference time per image on the CPU is only 22.19 ms. Compared with existing mainstream models (e.g., YOLOv5, YOLOv8, YOLOv9, and YOLOv10), EFA-YOLO exhibits a significant enhancement in detection accuracy (mAP) and inference speed, with model parameter amount is reduced by 94.6 and the inference speed is improved by 88 times.

EFA-YOLO: An Efficient Feature Attention Model for Fire and Flame Detection

TL;DR

An efficient and lightweight flame detection model, EFA-YOLO (Efficient Feature Attention YOLO), which exhibits a significant enhancement in detection accuracy (mAP) and inference speed, with model parameter amount reduced by 94.6 and the inference speed improved by 88 times.

Abstract

As a natural disaster with high suddenness and great destructiveness, fire has long posed a major threat to human society and ecological environment. In recent years, with the rapid development of smart city and Internet of Things (IoT) technologies, fire detection systems based on deep learning have gradually become a key means to cope with fire hazards. However, existing fire detection models still have many challenges in terms of detection accuracy and real-time performance in complex contexts. To address these issues, we propose two key modules: EAConv (Efficient Attention Convolution) and EADown (Efficient Attention Downsampling). The EAConv module significantly improves the feature extraction efficiency by combining an efficient attention mechanism with depth-separable convolution, while the EADown module enhances the accuracy and efficiency of feature downsampling by utilizing spatial and channel attention mechanisms in combination with pooling operations. Based on these two modules, we design an efficient and lightweight flame detection model, EFA-YOLO (Efficient Feature Attention YOLO). Experimental results show that EFA-YOLO has a model parameter quantity of only 1.4M, GFLOPs of 4.6, and the inference time per image on the CPU is only 22.19 ms. Compared with existing mainstream models (e.g., YOLOv5, YOLOv8, YOLOv9, and YOLOv10), EFA-YOLO exhibits a significant enhancement in detection accuracy (mAP) and inference speed, with model parameter amount is reduced by 94.6 and the inference speed is improved by 88 times.
Paper Structure (16 sections, 5 equations, 6 figures, 2 tables)

This paper contains 16 sections, 5 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Overview
  4. EAConv
  5. EADown
  6. Experimental Details
  7. Dataset
  8. Experimental environment
  9. Evaluation metrics
  10. Experimental Results and Discussion Analysis
  11. Comparative Experiment
  12. Detection Result
  13. Ablation Study
  14. Error Analysis
  15. Case Studies
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Shows the EFA-YOLO overall framework.
  • Figure 2: Shows the EAConv module.
  • Figure 3: Shows the EADown module.
  • Figure 4: Part of the dataset sample display.
  • Figure 5: Compare the detection results of the experimental models.
  • ...and 1 more figures