Abstract
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.
Highlights
In recent years, the unmanned surface vehicle (USV) as a typical automatic unmanned system has made considerable and rapid development
It is notable that the proposed YOLOV3-dense has a slightly higher convergence speed compared with YOLOV3 in the early stages of training, which means the weights of the proposed method could be trained with a lower time cost
This study proposed an improved YOLOV3 model by fusing DenseNet to detect sea surface targets under different environmental conditions, which is expected to enhance the environmental adaptability of the USV during a long-term task
Summary
The unmanned surface vehicle (USV) as a typical automatic unmanned system has made considerable and rapid development It is playing an important role in both military and civilian missions to reduce human casualties as well as to create mission efficiencies, covering submarine tracking, environmental monitoring, patrol, reconnaissance, and so on [1]. To achieve superior perception performance, the USV generally requires employing heterogeneous sensors covering radar, lidar, camera, and infrared sensors [6] They provide advantages of computer vision in terms of power consumption, size, weight, cost, and the readability of data, unlike radar or LIDAR, which may require heavy equipment placed on the vehicle [7,8,9].
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