Deep Learning Methods in Unmanned Underwater Vehicles

Abstract

Unmanned underwater vehicles (ROV/AUV) are robotic systems that can float underwater, are autonomous and remotely controlled. Nowadays, the Navy has focused on the operational use of unmanned underwater vehicles in the defense industry and in many areas, and has increased interest in this issue. Unmanned underwater vehicles. Unmanned underwater vehicles are carried out in civilian and military applications for different and varied purposes like protection of national sources, protection of environmental sources and researchs about that, miscellaneous construction activities, police of coastal and country. Also they can use civil and military applications and they helped they have helped with much of the academic and industrial research done in recent years. To sum up they are remotely controlled vehicles with observation and exploration features. This article discusses image processing and deep learning techniques in unmanned underwater vehicles. Also it presents an in-depth review of the artificial intelligence technique and aims to contribute to our country's defense industry. The options that will enable the vehicle to succeed in autonomous missions are mentioned. The Raspberry Pi 3 microprocessor was used in autonomous missions. The Raspberry Pi Camera Module, which is compatible with the Raspberry Pi 3, is preferred. Python was used as a programming language during software process. Objects in the images taken from the camera have been identified using the OpenCV library and deep learning. The TensorFlow library which deep learning library, was used for object detection and tracking. At the beginning The Faster-RCNN-Inception-V2 model was used as the Model. However, Faster-RCNN-Inception-V2 model and Raspberry Pi 3 FPS cooperation working did not show a good performance. For this reason, the SSDLite-MobileNet-V2 model, which is fast enough for most real-time object detection applications, is preferred.