Journal of Safety and Environment

To accurately detect fire and smoke in the complex disturbed environments of the wildland-urban interface， this paper proposes an enhanced detection model based on YOLOv8， named ViT–YOLOv8. This model introduces a Re-Parameterized Vision Transformer (RepViT) module into the backbone network of YOLOv8. Unlike traditional convolutional networks that mix spatial and channel information， this module processes spatial feature extraction and channel feature fusion separately， allowing these two processes to operate independently without interference. This enhances the model’s ability to capture and utilize key features more effectively. Additionally， by leveraging structural re-parameterization， the multi-branch topology of the token mixer is merged into a single depthwise convolution layer， optimizing the allocation of computational resources and improving operational efficiency. Furthermore， a Bottleneck Transformers Network (BoTNet) module integrated with a Ghost Convolutional layer (GhostConv) is incorporated after the spatial pyramid pooling layer in the backbone network. GhostConv increases the number of channels at a low computational cost， enabling the extraction of critical information from the original feature maps. A multi-head self-attention mechanism is introduced into the last three convolutional layers， allowing the model to consistently focus on crucial contextual information within feature maps under complex environmental conditions. This significantly enhances the model’s anti-interference capability while maintaining relatively low computational complexity. In addition to collecting high-quality images of fires in the wildland-urban interface， images of strong light sources that could be mistaken for fires were also gathered as interference samples， simulating the complex lighting conditions typical of such regions and improving the model's robustness. The constructed dataset comprises 4 268 images annotated into three categories: fire， smoke， and other. After training for 650 epochs on this dataset， comparative experiments were conducted with the baseline model YOLOv8n. Experimental results demonstrate that， compared to YOLOv8n， the proposed model achieved improvements in mean average precision (M_P)， precision (P)， and recall (R)， increasing from 96.8%， 84.4%， and 97.5% to 97.7%， 84.9%， and 98.0%， respectively. Moreover， ViT–YOLOv8 not only maintained high detection accuracy for fire and smoke targets but also exhibited superior precision in identifying interference items， showcasing excellent anti-interference capability. The model improves both global information capture and local detail recognition， facilitating more accurate detection of fires and their early smoke signs in the complex environment of the wildland-urban interface.