论文标题
紧固件：快速铁路紧固件检测器
FasteNet: A Fast Railway Fastener Detector
论文作者
论文摘要
在这项工作中，引入了一种新型的高速铁路紧固件检测器。这个被称为Fastenet的完全卷积网络已经预言了边界框的概念，并直接在预测的显着性图上执行检测。 Fastenet使用转置卷积和跳过连接，网络的有效接受场比紧固件的平均尺寸大于1.5 $ \ tims $ $，使网络能够以置信度很高，而无需牺牲输出分辨率。此外，由于显着地图方法，该网络能够投票通过每个紧固件的紧固件30次，从而提高了预测准确性。 Fastenet能够在NVIDIA GTX 1080上以110 fps的速度运行，同时获得1600美元的输入$ \ times $ 512，平均每个图像为14个紧固件。我们的来源在这里开放：https：//github.com/jjshoots/dlver_fastenet.git
In this work, a novel high-speed railway fastener detector is introduced. This fully convolutional network, dubbed FasteNet, foregoes the notion of bounding boxes and performs detection directly on a predicted saliency map. Fastenet uses transposed convolutions and skip connections, the effective receptive field of the network is 1.5$\times$ larger than the average size of a fastener, enabling the network to make predictions with high confidence, without sacrificing output resolution. In addition, due to the saliency map approach, the network is able to vote for the presence of a fastener up to 30 times per fastener, boosting prediction accuracy. Fastenet is capable of running at 110 FPS on an Nvidia GTX 1080, while taking in inputs of 1600$\times$512 with an average of 14 fasteners per image. Our source is open here: https://github.com/jjshoots/DL\_FasteNet.git
