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Multitask Attention Network for Lane Detection and Fitting
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 33:1066-1078
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Many CNN-based segmentation methods have been applied in lane marking detection recently and gain excellent success for a strong ability in modeling semantic information. Although the accuracy of lane line prediction is getting better and better, lane markings' localization ability is relatively weak, especially when the lane marking point is remote. Traditional lane detection methods usually utilize highly specialized handcrafted features and carefully designed postprocessing to detect the lanes. However, these methods are based on strong assumptions and, thus, are prone to scalability. In this work, we propose a novel multitask method that: 1) integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and 2) predicts the position of vanishing line. A novel lane fitting method based on vanishing line prediction is also proposed for sharp curves and nonflat road in this article. By integrating segmentation, specialized handcrafted features, and fitting, the accuracy of location and the convergence speed of networks are improved. Extensive experimental results on four-lane marking detection data sets show that our method achieves state-of-the-art performance.
- Subjects :
- Computer Networks and Communications
Computer science
business.industry
Pattern recognition
02 engineering and technology
Computer Science Applications
Artificial Intelligence
Position (vector)
Attention network
Line (geometry)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Point (geometry)
Lane detection
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....269bc61c8192f337abdf853c10dcd00f