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Line segment detection is an essential task in computer vision and imageanalysis, as it is the critical foundation for advanced tasks such as shapemodeling and road lane line detection for autonomous driving. We present arobust topological graph guided approach for line segment detection in lowquality binary images (hence, we call it TGGLines). Due to the graph-guidedapproach, TGGLines not only detects line segments, but also organizes thesegments with a line segment connectivity graph, which means the topologicalrelationships (e.g., intersection, an isolated line segment) of the detectedline segments are captured and stored; whereas other line detectors only retaina collection of loose line segments. Our empirical results show that theTGGLines detector visually and quantitatively outperforms state-of-the-art linesegment detection methods. In addition, our TGGLines approach has the followingtwo competitive advantages: (1) our method only requires one parameter and itis adaptive, whereas almost all other line segment detection methods requiremultiple (non-adaptive) parameters, and (2) the line segments detected byTGGLines are organized by a line segment connectivity graph.

TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images