Fully Convolutional Line Parsing

22 Apr 2021  ·  Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma ·

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 them with each line's center position, length, and angle. Based on empirical observation of the distribution of line angles in real image datasets, we further customize the design of convolution kernels of our fully convolutional network to effectively exploit such statistical priors. 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. Source code https://github.com/Delay-Xili/F-Clip.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Line Segment Detection wireframe dataset F-Clip sAP5 64.3 # 1
sAP10 68.3 # 2
Line Segment Detection York Urban Dataset F-Clip sAP5 28.5 # 1
sAP10 30.8 # 1