Fire Detection From Image and Video Using YOLOv5

TL;DR

This paper presents an improved YOLOv5-based deep learning model for fire detection in images and videos, emphasizing small target detection, real-time performance, and robustness under various lighting conditions.

Contribution

The paper introduces an enhanced YOLOv5 architecture with expanded feature extraction and pyramid promotion, achieving superior fire detection accuracy and speed.

Findings

Achieved 90.5% mAP in fire detection

F1 score of 0.88 for fire and smoke detection

Real-time detection at 0.12 seconds per frame

Abstract

For the detection of fire-like targets in indoor, outdoor and forest fire images, as well as fire detection under different natural lights, an improved YOLOv5 fire detection deep learning algorithm is proposed. The YOLOv5 detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLOv5 attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke with mAP 90.5% and f1 score 88%. Overall, the Fire-YOLOv5 detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects with F1 score 0.88. When the input image size is 416 x 416 resolution, the average detection time is 0.12 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations. The proposed system shows an improved approach in all fire detection metrics such as precision, recall, and mean average precision.