Ultra Fast Structure-aware Deep Lane Detection

Qin, Zequn; Wang, Huanyu; Li, Xi

doi:10.48550/arxiv.2004.11757

Cited by 14 publications

(22 citation statements)

References 22 publications

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“…Quantitative results. To verify the effectiveness of our proposed method, we compared it with state-of-the-art algorithms based on either segmentation or object detection, including SCNN [12], LaneNet(+H-Net) [10], EL-GAN [4], PointLaneNet [2], FastDraw [14], ENet-SAD [5], ERFNet-E2E [20], SIM-CycleGAN+ERFNet [9], UFNet [16] and PINet [6].…”

Section: Resultsmentioning

confidence: 99%

“…Accuracy(%) FP FN SCNN [12] 0.29 0.0068 1.0 SIM-CycleGAN+ERFNet [9] 62.58 0.9886 0.9909 UFNet [16] 65.53 0.5680 0.6546 PINet(4H) [6] 36.31 0.4886 0.8988 FOLOLane(ours) 84.36 0.3964 0.3841…”

Section: Methodsmentioning

confidence: 99%

“…A lane marker instance was clustered based on the trained feature embedding. [16] formulated lane marker detection as a pixel-wise classification problem for each row of an image. A specific feature map was predicted to indicate the position of a lane marker on each row.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with existing works [2,19,16,10,12], the proposed approach concentrates the capabilities of CNN on a local scale, which is suitable for CNN's limited FOV, and significantly reduces the complexity of the task and the dimension of the output. As a result, the compact output leads to stable and efficient training without additional effort in network architecture design and data collection.…”

Section: Introductionmentioning

confidence: 99%

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Focus on Local: Detecting Lane Marker from Bottom Up via Key Point

Jin

Zhou

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

113

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Mainstream lane marker detection methods are implemented by predicting the overall structure and deriving parametric curves through post-processing. Complex lane line shapes require high-dimensional output of CNNs to model global structures, which further increases the demand for model capacity and training data. In contrast, the locality of a lane marker has finite geometric variations and spatial coverage. We propose a novel lane marker detection solution, FOLOLane, that focuses on modeling local patterns and achieving prediction of global structures in a bottom-up manner. Specifically, the CNN models lowcomplexity local patterns with two separate heads, the first one predicts the existence of key points, and the second refines the location of key points in the local range and correlates key points of the same lane line. The locality of the task is consistent with the limited FOV of the feature in CNN, which in turn leads to more stable training and better generalization. In addition, an efficiency-oriented decoding algorithm was proposed as well as a greedy one, which achieving 36% runtime gains at the cost of negligible performance degradation. Both of the two decoders integrated local information into the global geometry of lane markers. In the absence of a complex network architecture design, the proposed method greatly outperforms all existing methods on public datasets while achieving the best state-of-the-art results and real-time processing simultaneously.

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Section: Resultsmentioning

confidence: 99%

“…Accuracy(%) FP FN SCNN [12] 0.29 0.0068 1.0 SIM-CycleGAN+ERFNet [9] 62.58 0.9886 0.9909 UFNet [16] 65.53 0.5680 0.6546 PINet(4H) [6] 36.31 0.4886 0.8988 FOLOLane(ours) 84.36 0.3964 0.3841…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Focus on Local: Detecting Lane Marker from Bottom Up via Key Point

Jin

Zhou

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

113

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“…This method improves the performance of the model while keeping the model lightweight. [22] defines lane detection as a task to find the collection of lane lines location in certain rows of the image, and this row-based classification uses global features.…”

Section: Lane Detectionmentioning

confidence: 99%

YOLOP: You Only Look Once for Panoptic Driving Perception

Wu¹,

Liao²,

Zhang³

et al. 2021

Preprint

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A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS) and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models will be released at https://github.com/hustvl/YOLOP.

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