RONELDv2: A faster, improved lane tracking method

Chng, Zhe Ming; Lew, Joseph Mun Hung; Lee, Jimmy Addison

doi:10.48550/arxiv.2202.13137

Cited by 2 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Segmentation-based methods [1,2,10,16,17,[20][21][22][23][24][25][26][27][28][29] view lane detection as a per-pixel classification task. To handle the classification problem of lane line points, some prior arts [28] treat each lane line as a category for segmentation.…”

Section: Related Workmentioning

confidence: 99%

Dense Hybrid Proposal Modulation for Lane Detection

Zhao

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

In this paper, we present a dense hybrid proposal modulation (DHPM) method for lane detection. Most existing methods perform sparse supervision on a subset of high-scoring proposals, while other proposals fail to obtain effective shape and location guidance, resulting in poor overall quality. To address this, we densely modulate all proposals to generate topologically and spatially high-quality lane predictions with discriminative representations. Specifically, we first ensure that lane proposals are physically meaningful by applying single-lane shape and location constraints. Benefitting from the proposed proposal-to-label matching algorithm, we assign each proposal a target ground truth lane to efficiently learn from spatial layout priors. To enhance the generalization and model the inter-proposal relations, we diversify the shape difference of proposals matching the same ground-truth lane. In addition to the shape and location constraints, we design a quality-aware classification loss to adaptively supervise each positive proposal so that the discriminative power can be further boosted. Our DHPM achieves very competitive performances on four popular benchmark datasets. Moreover, we consistently outperform the baseline model on most metrics without introducing new parameters and reducing inference speed. The codes of our method are available at https://github.com/wuyuej/DHPM.

show abstract

Section: Related Workmentioning

confidence: 99%

Dense Hybrid Proposal Modulation for Lane Detection

Zhao

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…Chng et al improved a lane detection method named RONELD by making it more robust to detect lane changes. Their advanced method named RONELDV2 detected lane point variance for finding more accurate lane parameters, which accelerated their method’s performance [ 31 ]. Lee et al proposed a novel lightweight lane detection method named DSUNet for detecting the lanes in real-time.…”

Section: Literature Reviewmentioning

confidence: 99%

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Khan

Haque

Hasan

et al. 2022

Sensors

View full text Add to dashboard Cite

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

show abstract

RONELDv2: A faster, improved lane tracking method

Cited by 2 publications

References 27 publications

Dense Hybrid Proposal Modulation for Lane Detection

Dense Hybrid Proposal Modulation for Lane Detection

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Contact Info

Product

Resources

About