Fs-yolo: fire-smoke detection based on improved YOLOv7

Abstract

Fire has emerged as a major danger to the Earth’s ecological equilibrium and human well-being. Fire detection and alert systems are essential. There is a scarcity of public fire datasets with examples of fire and smoke in real-world situations. Moreover, techniques for recognizing items in fire smoke are imprecise and unreliable when it comes to identifying small objects. We developed a dual dataset to evaluate the model’s ability to handle these difficulties. Introducing FS-YOLO, a new fire detection model with improved accuracy. Training YOLOv7 may lead to overfitting because of the large number of parameters and the limited fire detection object categories. YOLOv7 struggles to recognize small dense objects during feature extraction, resulting in missed detections. The Swin Transformer module has been enhanced to decrease local feature interdependence, obtain a wider range of parameters, and handle features at several levels. The improvements strengthen the model’s robustness and the network’s ability to recognize dense tiny objects. The efficient channel attention was incorporated to reduce the occurrence of false fire detections. Localizing the region of interest and extracting meaningful information aids the model in identifying pertinent areas and minimizing false detections. The proposal also considers using fire-smoke and real-fire-smoke datasets. The latter dataset simulates real-world conditions with occlusions, lens blur, and motion blur. This dataset tests the model’s robustness and adaptability in complex situations. On both datasets, the mAP of FS-YOLO is improved by 6.4\(\%\) and 5.4\(\%\) compared to YOLOv7. In the robustness check experiments, the mAP of FS-YOLO is 4.1\(\%\) and 3.1\(\%\) higher than that of today’s SOTA models YOLOv8s, DINO.