论文标题
单级对象检测器的修改方法，该方法允许利用场景的时间行为以提高检测精度
Modification method for single-stage object detectors that allows to exploit the temporal behaviour of a scene to improve detection accuracy
论文作者
论文摘要
提出了一种简单的修改方法，用于单级通用对象检测神经网络，例如Yolo和SSD，它通过利用检测管道中场景的时间行为来提高视频数据的检测准确性。结果表明，使用此方法，基本网络的检测准确性可以得到很大改进，尤其是对于被遮挡和隐藏的对象。结果表明，修改后的网络比未修改的网络更容易以更自信地检测隐藏对象。提出了一种弱监督的培训方法，该方法允许训练修改的网络而无需任何其他带注释的数据。
A simple modification method for single-stage generic object detection neural networks, such as YOLO and SSD, is proposed, which allows for improving the detection accuracy on video data by exploiting the temporal behavior of the scene in the detection pipeline. It is shown that, using this method, the detection accuracy of the base network can be considerably improved, especially for occluded and hidden objects. It is shown that a modified network is more prone to detect hidden objects with more confidence than an unmodified one. A weakly supervised training method is proposed, which allows for training a modified network without requiring any additional annotated data.
