<br>无人水面舰艇水域目标识别方法及应用,IEEE Access

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无人水面舰艇水域目标识别方法及应用
IEEE Access ( IF 3.4 ) Pub Date : 2021-12-27 , DOI: 10.1109/access.2021.3138983
Liang Cheng ₁ , Baoyuan Deng ₂ , Yuan Yang ₂ , Jifang Lyu ₁ , Jicheng Zhao ₃ , Ke Zhou ₂ , Chunli Yang ₃ , Leigang Wang ₃ , Shiyuan Yang ₃ , Yunze He ₂

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水体目标识别是无人水面舰艇（USV）感知技术的关键挑战。在USV应用中，检测精度和推理时间都很重要，但很难取得平衡，单帧水目标检测在视频检测中表现不稳定。为了解决这些问题，采用了许多策略来提高 YOLOv4 的性能，包括网络剪枝、焦点损失函数、空白标签训练以及直方图归一化预处理。优化后的检测方法平均精度（mAP）达到81.74%，预测速度达到26.77帧/秒（FPS），满足USV导航要求。为了构建基于 USV 的集成水目标识别系统，通过海上 USV 实验创建了包含 9936 个图像的水目标数据集，其中使用了人机交互注释和马赛克数据增强方法。通过级联 Siamese-RPN 跟踪网络可以大大减轻漏检和误报的问题，并且可以使用局部对比度显着颜色检测方案来检索水目标的主要颜色。正在测试的系统称为“ME120”，包括嵌入式边缘计算平台（Nvidia Jetson AGX Xavier）。最后，在线数据集学习表明，与原始YOLOv4相比，改进后的YOLOv4以mAP下降0.79%的代价实现了FPS提高66.98%，离线导航实验验证了我们的系统在保持高识别能力的同时实现了高识别能力。稳健程度。

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Water Target Recognition Method and Application for Unmanned Surface Vessels

Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4’s performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called “ME120” includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

更新日期：2021-12-27

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