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Driving Scene Understanding Using Hybrid Deep Neural Network

机译:使用混合深度神经网络来了解驾驶场景

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Currently, artificial intelligence is used in many fields, especially real-time object recognition in vision is very efficient and powerful than other vision processing methods, and it is used for robots and autonomous vehicles. However, a more meaningful judgment by artificial intelligence requires recognizing the position of the detected object or the batch among the objects beyond the object detection and recognizing the situation. In other words, we can interpret that a person would measure a situation after seeing an object. In this paper, we propose a system to combine real-time object detection and situation recognition. We represent object detection and situation recognition by networks (DNN) with different characteristics. We propose an innovative system that can efficiently combine two networks.
机译:当前,人工智能被用于许多领域,尤其是视觉中的实时对象识别比其他视觉处理方法非常有效和强大,并且被用于机器人和自动驾驶汽车。然而,通过人工智能进行的更有意义的判断需要识别检测到的物体或物体之间的批次的位置,而不是物体检测并识别情况。换句话说,我们可以解释为一个人在看到一个物体后会测量一种情况。在本文中,我们提出了一种将实时目标检测与态势识别相结合的系统。我们通过具有不同特征的网络(DNN)来表示对象检测和情况识别。我们提出了一种创新的系统,可以有效地结合两个网络。

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