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Improving YOLOX network for multi-scale fire detection

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Abstract

Forest fire is a severe natural disaster, which leads to the destruction of forest ecology. At present, fire detection technology represented by convolutional neural network is widely used in forest resource protection, which can realize rapid analysis. However, in forest flame and smoke detection tasks, due to continuous expansion of the target range, a better detection effect cannot be achieved. This paper proposes an improved YOLOX method for multi-scale forest fire detection. This method proposes a novel feature pyramid model to reduce the information loss of high-level forest fire feature maps and enhance the representation ability of feature pyramids. Moreover, the method applies a small object data augmentation strategy to enrich the forest fire dataset, making it more suitable for the actual forest fire scene. According to the experimental results, the mAP of the model proposed in this paper reaches 79.64%, which is about 4.89% higher than the baseline network YOLOX. The method improves the accuracy of forest fire detection, reduces false alarms, and is suitable for real scenarios of forest fires.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 61872260) and National Key Research and Development Program of China (No. 2021YFB3300503).

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Authors and Affiliations

  1. College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, People’s Republic of China

    Taofang Wang & Li Wang

  2. College of Software, Taiyuan University of Technology, Taiyuan, 030024, People’s Republic of China

    Jun Wang, Chao Wang, Yi Lei & Rui Cao

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  1. Taofang Wang
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  2. Jun Wang
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  3. Chao Wang
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  4. Yi Lei
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  5. Rui Cao
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  6. Li Wang
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Correspondence to Rui Cao or Li Wang.

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Wang, T., Wang, J., Wang, C. et al. Improving YOLOX network for multi-scale fire detection. Vis Comput 40, 6493–6505 (2024). https://doi.org/10.1007/s00371-023-03178-1

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