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

TL;DR

EFA-YOLO is a lightweight, efficient fire detection model that improves accuracy and speed by integrating novel attention modules, making it suitable for real-time applications in complex environments.

Contribution

The paper introduces EAConv and EADown modules, enhancing feature extraction and downsampling efficiency, leading to a lightweight fire detection model with superior performance.

Findings

Model parameters reduced by 94.6% compared to existing models

Inference speed improved by 88 times on CPU

Achieves high detection accuracy with only 1.4M parameters

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.