mlsanityData to 7 Oct 2026

Multi Lane Detection

arXiv 2212.11533 ↗First posted 22 Dec 2022cs.CVcited by 0

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Abstract (arXiv)

Lane detection is a long-standing task and a basic module in autonomous driving. The task is to detect the lane of the current driving road, and provide relevant information such as the ID, direction, curvature, width, length, with visualization. Our work is based on CNN backbone DLA-34, along with Affinity Fields, aims to achieve robust detection of various lanes without assuming the number of lanes. Besides, we investigate novel decoding methods to achieve more efficient lane detection algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

12 of 19 references.

References · 12

  1. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
  2. 2017Deep Layer Aggregationcited by 436
  3. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  4. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  5. 2020Keep your Eyes on the Lane: Real-time Attention-guided Lane Detectioncited by 137
  6. 2018Towards End-to-End Lane Detection: an Instance Segmentation Approachcited by 129
  7. 20183D-LaneNet: End-to-End 3D Multiple Lane Detectioncited by 106
  8. 2020Key Points Estimation and Point Instance Segmentation Approach for Lane Detectioncited by 77
  9. 2019Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networkscited by 41
  10. 2021LaneAF: Robust Multi-Lane Detection with Affinity Fieldscited by 41
  11. 2018EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detectioncited by 35
  12. 2019Lane Detection and Classification using Cascaded CNNscited by 14

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