Li Yan, Guo Jiahong, Guo Xiaomin, Liu Kaizhou, Zhao Wentao, Luo Yeteng, Wang Zhenyu
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
Sensors (Basel). 2020 Aug 28;20(17):4885. doi: 10.3390/s20174885.
The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a long-term task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the real-time performance is available in practical ocean tasks for USV.
无人水面航行器（USV）因其高度自主性和机动性的优势，在海洋环境观测和海上安全等诸多任务中发挥着重要作用。高精度探测水面目标可确保后续任务的实施。然而，在USV执行长期任务时，灯光和水面环境的变化会影响目标检测方法的性能。因此，本文提出了一种将DenseNet融合到YOLOV3中的新型目标检测方法，以提高检测的稳定性，减少特征损失，同时目标特征在深度神经网络层中进行传递。用于训练和测试该方法的所有图像数据均于2019年11月在南海使用USV进行为期一个月的海上试验期间，在真实海洋环境中获取。实验结果表明，与现有方法相比，该方法的性能更适合变化的天气条件，并且在USV的实际海洋任务中具有实时性能。
