LineMarkNet: Line Landmark Detection for Valet Parking
Zizhang Wu, Yuanzhu Gan, Tianhao Xu, Rui Tang, Jian Pu

TL;DR
LineMarkNet is a deep learning system designed for accurate, real-time detection of line landmarks in valet parking, combining multi-view fusion, hierarchical reasoning, and temporal filtering to improve autonomous parking accuracy and stability.
Contribution
The paper introduces a lightweight, multi-task deep network for line landmark detection that fuses surround-view features into a unified BEV space and employs hierarchical graph reasoning, with a new dataset for validation.
Findings
Achieves superior accuracy over existing methods.
Operates in real-time on Qualcomm 820A platform.
Provides stable, smooth line detection through temporal filtering.
Abstract
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…
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Taxonomy
TopicsVehicle License Plate Recognition · Advanced Neural Network Applications · Autonomous Vehicle Technology and Safety
MethodsAttention Is All You Need · Byte Pair Encoding · Laplacian EigenMap · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Adam · Laplacian Positional Encodings · Dropout · Label Smoothing · Transformer
