Polynomial Regression Network for Variable-Number Lane Detection

Wang, Bingke; Wang, Zilei; Zhang, Yixin

doi:10.1007/978-3-030-58523-5_42

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…To verify the performance of the model in this paper, it is compared with existing models (ResNet-18, ResNet-34 [ 27 ], Enet [ 28 ], LaneNet [ 29 ], SCNN [ 30 ], ENet-SAD [ 31 ], RESA-50 [ 32 ], SGLD-34 [ 33 ], Res34-VP [ 34 ]) that conducted comparative experiments on the TuSimple test set, and the results are shown in Table 3 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Lane Line Detection Algorithm Based on Instance Segmentation

Cheng

Wang

Mao

2023

Sensors

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Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA’s TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Lane Line Detection Algorithm Based on Instance Segmentation

Cheng

Wang

Mao

2023

Sensors

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“…1 SDLane is available at https://www.42dot.ai/akit/dataset. In [32], a three-branched network regresses polynomial coefficients of each lane and estimates its starting and ending points. In [25], for computational efficiency, a network selects the location of each lane on a predefined set of rows only.…”

Section: Related Workmentioning

confidence: 99%

“…Most such techniques adopt the semantic segmentation framework [5,12,13,21,22,34], in which each pixel in an image is dichotomized into either lane or no-lane category. To preserve continuous lane structure in detection results, several attempts have been made, including curve fitting [21], polynomial regression [32], and adversarial training [5]. However, even these algorithms may fail to detect less visible lanes in cluttered scenes, because they use only local features and may miss parts of lanes.…”

Section: Introductionmentioning

confidence: 99%

Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

Jin¹,

Wonhui²,

Jeong³

et al. 2022

Preprint

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A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes.

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“…PolyLaneNet [28] uses a fully connected layer to directly predict the polynomial coefficients of lanes in the image plane. PRNet [29] decomposes lane detection into three parts: polynomial regression, initial classification and height regression. Method in [30] applies IPM and least square fitting to predict parabolic equations Fig.…”

Section: Related Workmentioning

confidence: 99%

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

Bai¹,

Chen²,

Fu³

et al. 2022

Preprint

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3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformerbased methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.

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Polynomial Regression Network for Variable-Number Lane Detection

Cited by 14 publications

References 19 publications

Research on Lane Line Detection Algorithm Based on Instance Segmentation

Research on Lane Line Detection Algorithm Based on Instance Segmentation

Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

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