基于深度学习的水下目标声学识别与定位技术研究

Research on Underwater Target Recognition and Location Technique Based on Deep Learning

摘要:

为实现自主水下潜器(Autonomous Underwater Vehicle，简称 AUV)的自主目标探测识别与定位任务，以侧扫声呐数据为依据，考虑到扫描式声呐成像的特点，针对金属球类目标，基于 Darknet 框架设计了一种轻量化深度学习目标识别模型，并结合人工特征进行目标特性分析。同时对声呐图像设计了有效的图像增强方法。实验表明：上述目标识别方法在保证目标识别准确率的同时，具有较高的目标识别速率，适于低功耗嵌入式平台部署。

Abstract:

In order to realize autonomous target detection，recognition and positioning task of autonomous underwater vehicle(AUV)，basing on the side-scan sonar data and considering the characteristics of side-scan sonar images，a lightweight deep learning target recognition model is designed in this paper based on Darknet framework for metal balls. And the characteristics of metal balls are analyzed combining with artificial characteristics. At the same time，an effective image enhancement method is designed for sonar images. Experiments show that the objects recognition method described in this paper not only ensures the accuracy of target recognition，but also provides a high processing rate，which is suitable for the low-power embedded platform.