  We aim for accurate and efficient line landmark detection for valet parking,
which is a long-standing yet unsolved problem in autonomous driving. To this
end, we present a deep line landmark detection system where we carefully design
the modules to be lightweight. Specifically, we first empirically design four
general line landmarks including three physical lines and one novel mental
line. The four line landmarks are effective for valet parking. We then develop
a deep network (LineMarkNet) to detect line landmarks from surround-view
cameras where we, via the pre-calibrated homography, fuse context from four
separate cameras into the unified bird-eye-view (BEV) space, specifically we
fuse the surroundview features and BEV features, then employ the multi-task
decoder to detect multiple line landmarks where we apply the center-based
strategy for object detection task, and design our graph transformer to enhance
the vision transformer with hierarchical level graph reasoning for semantic
segmentation task. At last, we further parameterize the detected line landmarks
(e.g., intercept-slope form) whereby a novel filtering backend incorporates
temporal and multi-view consistency to achieve smooth and stable detection.
Moreover, we annotate a large-scale dataset to validate our method.
Experimental results show that our framework achieves the enhanced performance
compared with several line detection methods and validate the multi-task
network's efficiency about the real-time line landmark detection on the
Qualcomm 820A platform while meantime keeps superior accuracy, with our deep
line landmark detection system.
