Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.

中文翻译：

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基于轮廓加深的航标智能识别系统

感测由导航标记表示的导航环境对于无人船和智能导航系统是一项重要的任务，并且可以通过识别来自摄像机的图像来执行感测。为了提高图像识别的准确性，本文将轮廓增强算法结合到基于多尺度注意力机制的导航标记分类模型中。实验结果表明，该方法将导航标记分类的准确性从95.98％提高到96.53％。基于分类模型，为长江南京航道局开发了智能导航标记识别系统，该模型由TensorFlow Serving进行部署和更新。