Abstract
Currently, the deep learning anchor-based object detection algorithm has been widely used in flame detection. However, it relies too much on manual setting of anchor hyper-parameters and is insensitive to the change of object shape. Therefore, the improved anchor-free algorithm FCOS is introduced. Firstly, the Center-ness branch is replaced by the IoU prediction branch to make the bounding box location more accurate; then the random copy-pasting small objects and Mosaic data augmentation methods are used to improve the detection accuracy of small objects. The experimental results on the self-built dataset show that the AP and speed of FCOS are better than anchor-based algorithms. The use of IoU prediction branch can improve the location accuracy of the algorithm; the use of two data augmentation methods can further improve the AP of the algorithm for small objects by 9.6%.
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Wang, H., Wu, X., Liu, J., Li, J. (2021). Research on Flame Detection Based on Anchor-Free Algorithm FCOS. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_15
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