基于YOLO神经网络的地面飞机检测

V. Kharchenko, I. Chyrka
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引用次数: 28

摘要

本文对最先进的目标检测方法的性能进行了基准测试,以无人机或卫星拍摄的航空图像中的地面飞机识别检测为例。基于“你只看一次”的方法,测试了两种流行的单阶段神经网络YOLO v.3和Tiny YOLO v.3。所考虑的用于目标检测的人工神经网络架构已经被训练并应用于特定创建的图像数据库。实验验证了该算法的检测能力、定位精度和实时处理速度。这种方法可以很容易地用于检测许多不同类别的地面物体。
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Detection of Airplanes on the Ground Using YOLO Neural Network
The presented paper benchmarks the performance of state-of-the-art methods of objects detection in the particular case of airplanes on the ground identification detection in aerial images taken from unmanned aerial vehicles or satellites. There were tested two popular single-stage neural networks YOLO v.3 and Tiny YOLO v.3 based on the “You Only Look Once” approach. The considered artificial neural network architectures for objects detection has been trained and applied over the specifically created image database. Experimental verification proves their high detection ability, location precision and realtime processing speed using modern graphics processing unit. That approach can be easily applied for detection of many different classes of ground objects.
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