He Wang
ABSTRACT
A fire detection method based on YOLOv5s fused with Swin Transformer is proposed. This is to address the shortcomings of traditional forest fire detection methods such as poor detection accuracy and low reliability. To achieve real-time detection of forest fires, this paper proposes an improved recognition method for YOLOv5s-SwinT. Based on the application of the Transformer model, it solves the shortcomings of the convolutional neural network such as localization of operation and global feature extraction. It achieves favorable results, but at the same time, there are still disadvantages such as poor detection of small targets. In this paper, the Swin Transformer and YOLOv5 convolutional neural network models are fused and applied to the machine vision task of forest fire detection. The α-IoU loss function is introduced to replace the GIOU loss function, and the CA attention mechanism lightweight module is incorporated into the backbone network to improve the overall network's feature extraction capability as well as to obtain high-quality and highly accurate localized image regions for bounding box generation and prediction, improving problems such as missed detection and poor detection accuracy of small targets. The experimental results show that the improved recognition method incorporating YOLOv5s-SwinT can achieve an mAP value of 74.2% in the forest fire detection task, which is 4.5% higher than YOLOv5s, and the GUI interface is designed to be deployed directly to the PC side to realize real-time fire detection requirements, providing an effective detection method for forest fire detection vision tasks.
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Index Terms
- Fusion of YOLOv5s and Swin Transformer for forest fire detection
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