Bezier Line Object Detection
Detecting small and thin objects is the most challenging part of object detection in general. The novel object detectors today contribute heavily in detecting generic objects that are mostly big and visible making it easier for the detectors to extract features and creating bounding boxes over them. Those detectors often fail in detecting objects in specialized applications that are either very small or thin such as lines/curves. Existing approaches to detect lines are mostly concentrated in detecting polylines that are often hard to annotate, uses more computation, formulated in recurrent ways. To solve this, our algorithm suggests a generic solution to detect bezier lines using a single-shot object detection approach. Instead of detecting individual polylines, our algorithm suggests a formulation to detect individual lines using their very control points. This method has several advantages over previous methods. It does not just create lines instead of boxes but detect the coordinates of the individual lines as a whole with good accuracy. Comparable to polylines, the targets are easily annotatable and use less computation power since require only control point coordinates to generate accurate results. We evaluated our results on the TUSimple lane dataset and compared the results with other line detectors with satisfactory results, hereby demonstrating the ability of this algorithm to work on generalized datasets as well. The source code is available on Github.
Rostock, Univ., Master Thesis, 2021
Lukas, Uwe von