We present a one-stage Fully Convolutional Line Parsing network (F-Clip) that detects line segments from images. The proposed network is very simple and flexible with variations that gracefully trade off between speed and accuracy for different applications. F-Clip detects line segments in an end-to-end fashion by predicting each line's center position, length, and angle. We further customize the design of convolution kernels of our fully convolutional network to effectively exploit the statistical priors of the distribution of line angles in real image datasets. We conduct extensive experiments and show that our method achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU. Such inference speed makes our method readily applicable to real-time tasks without compromising any accuracy of previous methods. Moreover, when equipped with a performance-improving backbone network, F-Clip is able to significantly outperform all state-of-the-art line detectors on accuracy at a similar or even higher frame rate. In other word, under same inference speed, F-Clip always achieving best accuracy compare with other methods. Source code https://github.com/Delay-Xili/F-Clip.
翻译:我们展示了一个从图像中检测线条段的单阶段全演线剖析网络(F-Clip),它从图像中检测线条段。提议的网络非常简单灵活,有各种变化,在不同应用程序的速度和准确性之间进行优于交换。F-Clip通过预测每个线条的中心位置、长度和角度,以端到端的方式检测线条段。我们进一步定制了我们完全革命网络的组合内核的设计,以有效利用真实图像数据集中线角分布的统计前端。我们进行了广泛的实验,并表明我们的方法在效率和准确性之间实现了显著的更佳的交换,结果在单一的GPU上形成高达73 FPS的实时线条探测器。这种推断速度使得我们的方法很容易适用于实时任务,而不会影响以前方法的准确性能改进的主干网。此外,F-Clip能够大大超越所有在类似甚至更高的框架速率上的状态-艺术线探测器。在其它单词中,将AD-Fliply/Flip 的精确度方法与其它的精确度进行对比。