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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >MiNet: Efficient Deep Learning Automatic Target Recognition for Small Autonomous Vehicles
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MiNet: Efficient Deep Learning Automatic Target Recognition for Small Autonomous Vehicles

机译：Minet：高效的深度学习自动目标对小型自动车辆的自动目标识别

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摘要

On-the-fly automatic target recognition (ATR) is a challenge for small autonomous vehicles performing remote sensing. Advances in deep learning have made object detection practicable on data from a variety of sensor types, and neural network-based object detector models trained on big data sets of natural images are commonly adapted to the remote sensor (RS) domain via transfer learning. However, constraints of small vehicle hardware, such as computational performance and battery power, limit capacity for running deep learning models onboard. Standard pretrained object detection models, such as YOLO and R-CNN, contain large convolutional neural networks requiring tens to hundreds of billions of floating-point operations to distinguish between many natural image object classes. Such large models may be overly complex for ATR tasks in RS data. This letter describes an efficient deep learning model, MiNet, developed to detect mine-like objects in sonar data. It was built in Keras and TensorFlow and trained entirely on real and synthetically generated sonar data using an incremental training procedure. MiNet was successfully deployed onboard small OceanServer Iver3 autonomous underwater vehicles during the REBOOT sea trial and predicted the latitude, longitude, and class of objects detected in sonar images within minutes of the completion of each mission leg.
机译：在线自动目标识别（ATR）对于执行遥感的小型自动车辆是一项挑战。深度学习的进步对来自各种传感器类型的数据进行了可行的对象检测，并且在大数据集上培训的基于神经网络的对象检测器模型通常通过传输学习适应远程传感器（RS）域。然而，小型车辆硬件的限制，如计算性能和电池电量，限制在板上运行深度学习模型的容量。标准净化对象检测模型，例如YOLO和R-CNN，包含需要数十亿浮点操作的大型卷积神经网络，以区分许多自然图像对象类。对于RS数据中的ATR任务可能会过于复杂。这封信描述了一个高效的深度学习模型，Minet，用于检测声纳数据中的雷米物体。它建于克拉斯和Tensorflow，并使用增量培训程序完全培训真实的和综合的声纳数据。在重新启动海上试验期间，Minet成功部署了船上的小型Oceanserver Iver3自治水下车辆，并预测了在每个任务腿完成的几分钟内在声纳图像中检测到的物体的纬度，经度和类。

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