  Line segment detection is an essential task in computer vision and image
analysis, as it is the critical foundation for advanced tasks such as shape
modeling and road lane line detection for autonomous driving. We present a
robust topological graph guided approach for line segment detection in low
quality binary images (hence, we call it TGGLines). Due to the graph-guided
approach, TGGLines not only detects line segments, but also organizes the
segments with a line segment connectivity graph, which means the topological
relationships (e.g., intersection, an isolated line segment) of the detected
line segments are captured and stored; whereas other line detectors only retain
a collection of loose line segments. Our empirical results show that the
TGGLines detector visually and quantitatively outperforms state-of-the-art line
segment detection methods. In addition, our TGGLines approach has the following
two competitive advantages: (1) our method only requires one parameter and it
is adaptive, whereas almost all other line segment detection methods require
multiple (non-adaptive) parameters, and (2) the line segments detected by
TGGLines are organized by a line segment connectivity graph.
