Outdoor fire detection using the improved YOLO Nano version 8 model

Document Type : Original Article

Authors

1 M.Sc. Student in Artificial Intelligence and Robotics, Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Iran

2 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Iran

3 Associate Professor, Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Iran

Abstract

The YOLOv8-nano model is designed as a lightweight network for real-time object detection, but it has limitations in detecting small objects such as fire and smoke. In this study, the proposed model improves performance for small objects by extending the feature pyramid to the P2 scale, adding the CBAM attention module in the intermediate and deep layers, and replacing standard convolution with GhostConv in the PAN pathway. The P2 scale preserves the spatial details of small objects, while the FPN pathway transfers semantic information from deeper layers. The CBAM module, by combining channel and spatial attention, enhances features related to small objects and reduces background noise. GhostConv also increases model efficiency by reducing parameters and computational complexity. Experimental results on the dataset show that the proposed model provides an optimal balance between detection accuracy and computational cost, and achieves better accuracy than YOLOv8-n in real-time applications. These results indicate that the optimized architecture can offer an effective solution for detecting small objects in complex environments.

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References

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Applied and basic Machine intelligence research
Volume 4, Issue 1
May 2026
Pages 29-40

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