# Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection

arXiv: https://arxiv.org/abs/2409.17158 · HTML: https://mlsanity.com/paper/2409.17158/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [LaneNet: Real-Time Lane Detection Networks for Autonomous Driving](https://mlsanity.com/paper/1807.01726/) (2018)
- [Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1809.06323/) (2018)
- [Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer](https://mlsanity.com/paper/2002.01177/) (2020)
- [A Testbed for Cross-Dataset Analysis](https://mlsanity.com/paper/1402.5923/) (2014)
- [Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks](https://mlsanity.com/paper/1907.09438/) (2019)

## Built on by (most cited first, 2 of 2)

- [Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches](https://mlsanity.com/paper/2501.18494/) (2025)
- [Adapt, But Don't Forget: Fine-Tuning and Contrastive Routing for Lane Detection under Distribution Shift](https://mlsanity.com/paper/2507.18653/) (2025)

## Abstract (arXiv)

Transfer Learning has become one of the standard methods to solve problems to overcome the isolated learning paradigm by utilizing knowledge acquired for one task to solve another related one. However, research needs to be done, to identify the initial steps before inducing transfer learning to applications for further verification and explainablity. In this research, we have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. Lane detection is an important aspect of autonomous vehicles driving assistance system. In most circumstances, modern deep-learning-based lane recognition systems are successful, but they struggle with lanes with complex topologies. The proposed architecture, ERFCondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies like dense, curved and fork lines. The newly proposed technique was tested on two common lane detecting benchmarks, CULane and CurveLanes respectively, and two different backbones, ResNet and ERFNet. The researched technique with ERFCondLaneNet, exhibited similar performance in comparison to ResnetCondLaneNet, while using 33% less features, resulting in a reduction of model size by 46%.