# Object Keyline Similarity

In vision-based landing, runway corner keypoints are often cropped by the field of view during close approach, leaving point-based pose solvers without geometric constraints. The work extends object detection from keypoints to keylines: each object instance predicts a fixed, semantically ordered set of infinite lines, implemented on YOLO with a continuous Hesse normal parameterization and Object Keyline Similarity (OKLS) serving as evaluation metric, target assignment signal and regression loss. On runway data, the continuous Hesse form with OKLS gives the highest keyline detection accuracy among four parameterizations, and a fused head adds keylines without degrading keypoint accuracy or meaningfully increasing latency.

In vision-based landing, runway corner keypoints are often cropped by the field of view during close approach, leaving point-based pose solvers without geometric constraints. The work extends object detection from keypoints to keylines: each object instance predicts a fixed, semantically ordered set of infinite lines, implemented on YOLO with a continuous Hesse normal parameterization and Object Keyline Similarity (OKLS) serving as evaluation metric, target assignment signal and regression loss. On runway data, the continuous Hesse form with OKLS gives the highest keyline detection accuracy among four parameterizations, and a fused head adds keylines without degrading keypoint accuracy or meaningfully increasing latency.