Summary of Cross Dataset Analysis and Network Architecture Repair For Autonomous Car Lane Detection, by Parth Ganeriwala et al.
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection
by Parth Ganeriwala, Siddhartha Bhattacharyya, Raja Muthalagu
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research investigates the application of transfer learning for lane detection in autonomous vehicles. The goal is to identify the initial steps required for inducing transfer learning and verifying its effectiveness. To achieve this, the study conducts cross-dataset analysis and network architecture repair on a lane detection framework called CondlaneNet. The enhanced architecture, ERFCondLaneNet, is designed to overcome challenges in detecting complex lane topologies. The technique is evaluated on two benchmarks, CULane and CurveLanes, using ResNet and ERFNet as backbones. The results show that ERFCondLaneNet outperforms ResnetCondLaneNet while reducing the model size by 46%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lane detection in autonomous vehicles is crucial for driving assistance systems. This research uses transfer learning to improve lane detection accuracy. The study focuses on a specific framework, CondlaneNet, and proposes an enhanced architecture called ERFCondLaneNet. This new technique helps detect complex lane topologies like dense, curved, or fork lines. Two benchmarks, CULane and CurveLanes, are used to test the model’s performance. |
Keywords
» Artificial intelligence » Resnet » Transfer learning
