Abstract
Ship remote sensing target recognition is a critical task in various maritime applications, including surveillance, navigation assistance, and disaster management. However, traditional methods face challenges in detecting and recognizing ships in complex maritime environments, which include various types of ships, sea conditions, and environmental factors. In recent years, deep learning-based object detection algorithms have shown promising results in detecting and recognizing ships in remote sensing images. In this paper, we propose a ship remote sensing target recognition method based on the YOLOV5 algorithm. Our approach uses a deep convolutional neural network to extract high-level features from remote sensing images and detect and classify ships. The proposed method uses anchor-based object detection to identify ship locations and a multi-scale feature fusion strategy to capture different ship sizes and orientations. We also introduce a new ship dataset, which includes various ship types and sea conditions, to evaluate the performance of our proposed method. Experimental results show that our method outperforms other common ship detection algorithms in terms of detection accuracy. Our method can significantly contribute to improving ship detection and recognition in real-world maritime applications, especially in complex scenarios.
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Acknowledgements
I would like to thank Bei**g Institute of Spacecraft Environment Engineering for providing me with good equipment, and Yunwei Li, Yusen Ma, and **nan Zhang for their help.
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Hao, N., Li, Y., Ma, Y., Zhang, X. (2024). Ship Remote Sensing Target Recognition Based on YOLOV5. In: Li, S. (eds) Computational and Experimental Simulations in Engineering. ICCES 2023. Mechanisms and Machine Science, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-031-44947-5_44
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