Abstract
In recent years, the number and intensity of forest fires have been increasing due to climate change, causing great ecological and property losses. The recent advances in deep learning and object detection have made it possible to use efficient models to detection forest fires. To further improve the detection speed and accuracy of early forest fires, the deep learning-based methods are increasingly adopted. Considering the detection speed and accuracy, the MobileNet SSD model has a good performance. However, it does not perform well when detecting small objects, such as fire in the initial stage. To enhance the model’s detection performance for small objects, we proposed an improved feature pyramid network. To achieve real-time speed, replace SSD (Single Shot MultiBox Detector) with SSDLite, forming a MobileNetV2_SSDLite_FPN model. The experimental results indicate that this model achieves 89.7% mean Average Precision (mAP), 2.3 MB parameters, and 0.013 s average running time on our fire dataset.
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Acknowledgements
This work was supported by Provincial Key Platforms and Major Research Projects of Universities in Guangdong Province under No. 2021ZDZX3012 and 2021KTSCX187. The authors gratefully acknowledge all these supports.
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An, Y., Tang, J., Li, Y. (2022). A MobileNet SSDLite Model with Improved FPN for Forest Fire Detection. In: Wang, Y., Ma, H., Peng, Y., Liu, Y., He, R. (eds) Image and Graphics Technologies and Applications. IGTA 2022. Communications in Computer and Information Science, vol 1611. Springer, Singapore. https://doi.org/10.1007/978-981-19-5096-4_20
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DOI: https://doi.org/10.1007/978-981-19-5096-4_20
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