Image-Based Radar Cross Section Synthesis for a Cluster of Multiple Static Targets

Watanabe, Takuma

doi:10.1109/tim.2023.3246489

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Data enhancement can be divided into two main categories: single image transformation 27 and multi-image synthesis. 28 Single image transformation, including color adjustment, motion blur, and perspective transformation, [10][11][12] has proven effective in general object detection tasks by enhancing the diversity of training data. However, in the context of ice monitoring, the applicability of these traditional methods has been weakened to varying degrees.…”

Section: Data Augmentationmentioning

confidence: 99%

“…In the field of polar target recognition, the role of data enhancement is critical, mainly due to the scarcity and challenging nature of actual polar environmental data. Data enhancement can be divided into two main categories: single image transformation 27 and multi‐image synthesis 28 . Single image transformation, including color adjustment, motion blur, and perspective transformation, 10–12 has proven effective in general object detection tasks by enhancing the diversity of training data.…”

Section: Related Workmentioning

confidence: 99%

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An image augmentation‐based ice monitoring method for safe navigation of polar ships

Liang,

Wang,

Zhao

et al. 2024

Quality & Reliability Eng

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The navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships.

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