research-article

Fast and Accurate Lane Detection via Frequency Domain Learning

Authors:

Yulan GuoAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 890 - 898

https://doi.org/10.1145/3474085.3475267

Published: 17 October 2021 Publication History

Abstract

It is desirable to maintain both high accuracy and runtime efficiency in lane detection. State-of-the-art methods mainly address the efficiency problem by direct compression of high-dimensional features. These methods usually suffer from information loss and cannot achieve satisfactory accuracy performance. To ensure the diversity of features and subsequently maintain information as much as possible, we introduce multi-frequency analysis into lane detection. Specifically, we propose a multi-spectral feature compressor (MSFC) based on two-dimensional (2D) discrete cosine transform (DCT) to compress features while preserving diversity information. We group features and associate each group with an individual frequency component, which incurs only 1/7 overhead of one-dimensional convolution operation but preserves more information. Moreover, to further enhance the discriminability of features, we design a multi-spectral lane feature aggregator (MSFA) based on one-dimensional (1D) DCT to aggregate features from each lane according to their corresponding frequency components. The proposed method outperforms the state-of-the-art methods (including LaneATT and UFLD) on TuSimple, CULane, and LLAMAS benchmarks. For example, our method achieves 76.32% F1 at 237 FPS and 76.98% F1 at 164 FPS on CULane, which is 1.23% and 0.30% higher than LaneATT. Our code and models are available at https://github.com/harrylin-hyl/MSLD.

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Liang KMeng LLiu YLiu MWei WLiu STu WWang SZhou SLiu XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Simple Yet Effective: Structure Guided Pre-trained Transformer for Multi-modal Knowledge Graph ReasoningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681112(1554-1563)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681112
Liu YZhu LWan LWang X(2024)Masked frequency-color fusion network for video instance-level hazy lane detectionThe Visual Computer10.1007/s00371-024-03671-1Online publication date: 14-Oct-2024
https://doi.org/10.1007/s00371-024-03671-1
Liao MTian SZhang YHua GZou WLi XEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic SegmentationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611792(2199-2210)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611792
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Index Terms

Fast and Accurate Lane Detection via Frequency Domain Learning
1. Computing methodologies
  1. Artificial intelligence
2. Networks
  1. Network algorithms

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Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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New York, NY, United States

Publication History

Published: 17 October 2021

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National Natural Science Foundation of China
Science & Technology Major Project of Hubei Province
National Key Research and Development Program of China
Major Project for Technological Innovation of Hubei Province

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Liang KMeng LLiu YLiu MWei WLiu STu WWang SZhou SLiu XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Simple Yet Effective: Structure Guided Pre-trained Transformer for Multi-modal Knowledge Graph ReasoningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681112(1554-1563)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681112
Liu YZhu LWan LWang X(2024)Masked frequency-color fusion network for video instance-level hazy lane detectionThe Visual Computer10.1007/s00371-024-03671-1Online publication date: 14-Oct-2024
https://doi.org/10.1007/s00371-024-03671-1
Liao MTian SZhang YHua GZou WLi XEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic SegmentationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611792(2199-2210)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611792
Yang HLin SJiang RLu YWang H(2023)DQFORMER: Dynamic Query Transformer for Lane DetectionICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10097047(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10097047
Park JJohnson J(2023)RGB No More: Minimally-Decoded JPEG Vision Transformers2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.02139(22334-22346)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.02139
Wu MLi CYao Z(2022)Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and ChallengesApplied Sciences10.3390/app1216810312:16(8103)Online publication date: 12-Aug-2022
https://doi.org/10.3390/app12168103

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