Abstract
The task of destroying all types of scrapped ammunitions has always been extremely dangerous. Effective ammunition image segmentation algorithms can assist in reducing the risk of destruction missions. According to the uniqueness of the ammunition image segmentation task, we proposed an effective method for ammunition image segmentation named MIA-Net based on U-Net deep learning model. The Multi-scale Input Module (MIM) is used to supervise and revise the model at multiple levels, and the Weighted Attention Module (WAM) is used to represent features of different channels. The learning of multi-layer information is weighted and fused, which greatly increases the performance of the segmentation network model. Experiments demonstrate the effectiveness of the proposed method.
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Cao, H., Wang, Y., Li, M., Fan, H. (2022). MIA-Net: An Improved U-Net for Ammunition Segmentation. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_42
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DOI: https://doi.org/10.1007/978-3-031-13841-6_42
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