Lane Line Detection Research Articles

Slope-embedded ViT-based model for lane line detection under occlusions

Slope-embedded ViT-based model for lane line detection under occlusions

Multi-feature fusion dehazing based on CycleGAN

Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network’s recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network’s attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

Objective In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety. Methods On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model. Results In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%. Conclusions The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

Multi-Task Environmental Perception Methods for Autonomous Driving.

In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems.

A Nonlinear Convolutional Neural Network Algorithm for Autonomous Vehicle Lane Line Detection

A Nonlinear Convolutional Neural Network Algorithm for Autonomous Vehicle Lane Line Detection

InstLane Dataset and Geometry-Aware Network for Instance Segmentation of Lane Line Detection

Despite impressive progress, obtaining appropriate data for instance-level lane segmentation remains a significant challenge. This limitation hinders the refinement of granular lane-related applications such as lane line crossing surveillance, pavement maintenance, and management. To address this gap, we introduce a benchmark for lane instance segmentation called InstLane. To the best of our knowledge, InstLane constitutes the first publicly accessible instance-level segmentation standard for lane line detection. The complexity of InstLane emanates from the fact that the original data are procured using cameras mounted laterally, as opposed to traditional front-mounted sensors. InstLane encapsulates a range of challenging scenarios, enhancing the generalization and robustness of the lane line instance segmentation algorithms. In addition, we propose GeoLaneNet, a real-time, geometry-aware lane instance segmentation network. Within GeoLaneNet, we design a finer localization of lane proto-instances based on geometric features to counteract the prevalent omission or multiple detections in dense lane scenarios resulting from non-maximum suppression (NMS). Furthermore, we present a scheme that employs a larger receptive field to achieve profound perceptual lane structural learning, thereby improving detection accuracy. We introduce an architecture based on partial feature transformation to expedite the detection process. Comprehensive experiments on InstLane demonstrate that GeoLaneNet can achieve up to twice the speed of current State-Of-The-Artmethods, reaching 139 FPS on an RTX3090 and a mask AP of 73.55%, with a permissible trade-off in AP, while maintaining comparable accuracy. These results underscore the effectiveness, robustness, and efficiency of GeoLaneNet in autonomous driving.

Lane Attribute Classification Based on Fine-Grained Description.

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection.

CTFNet: Coarse-to-Fine Segmented Lane Line Detection in Complex Road Conditions

To maintain robustness in complex and uncontrollable real-world driving scenarios, this paper proposes a new segmentation-based coarse-to-fine lane line model (CTFNet). Which embeds dual-pathway attention (DPA) in the coarse segmentation encoder-decoder architecture to fuse high and low-level features with dual inputs, taking the strengths and complementing the weaknesses, and at the same time being able to capture more spatial detail information. However, the extracted lane line cues are limited in extreme conditions. As a result, a feature localization module (FLM) is proposed which extracts the global contextual information of the occluded region along the vertical and horizontal axes and determines lane line location by predicting the confidence of the lane lines based on the extracted information. Additionally, some regions of the initial feature map of the coarse segmentation network are difficult to distinguish between classes, the uncertain region refinement module (URRM) is designed in the fine stage to gradually refine the uncertain pixels using the relationship between adjacent features. Finally, the model is extensively tested on the tvtLANE data set, and the results show that CTFNet outperforms most state-of-the-art methods with an F1-measure of 91.48%, which not only reduces false detection but also maintains good robustness in extremely difficult scenarios.

Cascaded-LaneAFA: a single-stage traffic lane line detection network

Cascaded-LaneAFA: a single-stage traffic lane line detection network

Robust Lane Detection Method Based on Dynamic Region of Interest

Lane detection is an important component of vehicle assisted driving systems. Many lane line algorithms have been proposed, but lane line detection is still a challenging task in environments with road text or vehicle interference. To address these issues, this paper proposes a robust lane line detection method under structured roads. First, we propose a method based on the slope and length of a straight line and the distance of the line from the centre line to extract regions of interest reducing interference from other invalid regions. Afterwards, we use a new fusion method to fuse the extracted colour features with the gradient features, enhancing the lane features while greatly reducing the interference in the pixels. Next, we cut the extracted pixel feature maps in equal parts into several sub-maps and perform pixel statistics for each sub-map, replacing sub-maps with abnormal pixel values with adjacent sub-maps, which can effectively reduce noise. Finally, we use DBSCAN to cluster the pixels and fit the lane lines with least squares. The experimental results show that the method can effectively detect lane lines and has good robustness.

Lane Line Detection

Abstract: Lane line detection is a crucial component in the development of advanced driver assistance systems (ADAS) and autonomous vehicles. This research proposes a comprehensive approach to enhance the accuracy and robustness of lane line detection algorithms, addressing challenges such as varying road conditions, lighting conditions, and diverse road markings.The proposed methodology leverages computer vision techniques, including edge detection, color space transformations, and image filtering, to preprocess input images from onboard cameras. A novel algorithm for lane line extraction is introduced, combining feature extraction and geometric analysis to accurately identify lane markings. The algorithm adapts to different road scenarios by dynamically adjusting parameters based on environmental conditions. The proposed lane line detection system demonstrates promising results in terms of accuracy, adaptability, and real-time performance, showcasing its potential for practical implementation in autonomous vehicles and ADAS. The research contributes to the ongoing efforts in creating robust and reliable perception systems for safe and efficient autonomous navigation on roads.

OpenCV-based Lane Line Detection Method for Mountain Curves

In this paper, a new method based on the OpenCV computer vision library is proposed and used for the detection of mountain bike lane lines. Firstly, a series of pre-processing, HLS color filtering, grayscale map conversion, etc. are performed on the images captured by the on-board camera. Next, the lane line image is processed by Gaussian blur noise reduction, Canney edge detection and other techniques. Then, after masking for the region of interest, the lane lines are fitted after applying the Hough transform to obtain a lane line image that can be used for detection. Finally, to enhance the visual effect, the fitted lane line image is filled and displayed superimposed with the original image. The experimental results show that the proposed method has high detection accuracy and efficiency in general mountain road scenarios, and it plays a certain role in assisting the driver to drive.

A Fast and Accurate Lane Detection Method Based on Row Anchor and Transformer Structure.

Lane detection plays a pivotal role in the successful implementation of Advanced Driver Assistance Systems (ADASs), which are essential for detecting the road's lane markings and determining the vehicle's position, thereby influencing subsequent decision making. However, current deep learning-based lane detection methods encounter challenges. Firstly, the on-board hardware limitations necessitate an exceptionally fast prediction speed for the lane detection method. Secondly, improvements are required for effective lane detection in complex scenarios. This paper addresses these issues by enhancing the row-anchor-based lane detection method. The Transformer encoder-decoder structure is leveraged as the row classification enhances the model's capability to extract global features and detect lane lines in intricate environments. The Feature-aligned Pyramid Network (FaPN) structure serves as an auxiliary branch, complemented by a novel structural loss with expectation loss, further refining the method's accuracy. The experimental results demonstrate our method's commendable accuracy and real-time performance, achieving a rapid prediction speed of 129 FPS (the single prediction time of the model on RTX3080 is 15.72 ms) and a 96.16% accuracy on the Tusimple dataset-a 3.32% improvement compared to the baseline method.

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection. As deep learning and computer vision technologies evolve, a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field. However, owing to the simple appearance of lane lines and the lack of distinctive features, it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines. The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines. To address the aforementioned challenges, we propose a novel deep learning approach for lane line detection. This method leverages the Swin Transformer in conjunction with LaneNet (called ST-LaneNet). The experience results showed that the true positive detection rate can reach 97.53% for easy lanes and 96.83% for difficult lanes (such as scenes with severe occlusion and extreme lighting conditions), which can better accomplish the objective of detecting lane lines. In 1000 detection samples, the average detection accuracy can reach 97.83%, the average inference time per image can reach 17.8 ms, and the average number of frames per second can reach 64.8 Hz. The programming scripts and associated models for this project can be accessed openly at the following GitHub repository: https://github.com/Duane711/Lane-line-detection-ST-LaneNet.

Multi-frame network feature fusion model and self-attention mechanism for vehicle lane line detection

The traditional lane detection networks mainly use independent single frame images to extract features first and then detect them, which cannot deal with the scene with complex background well. Therefore, this paper proposes a lane parallel detection network based on multi-frame network feature fusion model and self-attention mechanism according to the scene characteristics that vehicles can obtain continuous images during normal driving. Firstly, a parallel feature extraction structure is designed. On the one hand, a single frame network with high precision is used to extract the features of the current frame. On the other hand, a lightweight multi-frame network is designed to extract features of low-resolution multi-frame temporal images. And the recurrent neural network module is used to fuse the extracted temporal features and obtain multi-frame features. Self-attention mechanism can effectively capture the relevant information of internal features. Then the fusion module of single frame feature, multi-frame feature and self-attention feature is designed. The feature map of lane line is output by up-sampling network. The experimental results show that the network in this paper has significant improvement in both objective detection accuracy and subjective effect compared with other methods.

Mobip: a lightweight model for driving perception using MobileNet.

The visual perception model is critical to autonomous driving systems. It provides the information necessary for self-driving cars to make decisions in traffic scenes. We propose a lightweight multi-task network (Mobip) to simultaneously perform traffic object detection, drivable area segmentation, and lane line detection. The network consists of a shared encoder for feature extraction and two decoders for handling detection and segmentation tasks collectively. By using MobileNetV2 as the backbone and an extremely efficient multi-task architecture to implement the perception model, our network has great advantages in inference speed. The performance of the multi-task network is verified on a challenging public Berkeley Deep Drive(BDD100K) dataset. The model achieves an inference speed of 58 FPS on NVIDIA Tesla V100 while still maintaining competitive performance on all three tasks compared to other multi-task networks. Besides, the effectiveness and efficiency of the multi-task architecture are verified via ablative studies.

Vehicle-based LIDAR for Lane Line Detection

A lane line detection method based on adaptive thresholding of raster map is proposed for assisting driving by fitting lane lines in the process of automatic driving in ports. Firstly, the original laser point cloud is divided into regions of interest to retain the point clouds containing lane lines as well as to remove irrelevant point clouds. Secondly, the raster map is divided based on the intensity of the point cloud, and the ground points are filtered as well as the point cloud of the lane line part is retained. Then the lane lines are segmented by adaptive thresholding for the retained grid, and finally RANSAC is used to fit the lane lines. The experiments prove that the method can extract the lane line features well.

Advances and Future Directions in Road Lane Line Detection Techniques for Autonomous Driving Systems

This paper explores various road lane line detection techniques essential to autonomous driving and driver assistance systems.

A modulized lane‐follower for driverless vehicles using multi‐frame

AbstractAs a fundamental function, lane following plays an important role for driverless vehicles. Unfortunately, lane followers generally confront great difficulty in lane line missed situations caused by vague line, shadows etc. However, for most lane line missed situation, clues of the line may be hidden in prior view of it. Consequently, a lane follower called UNL Lane Follower, which contains two deep learning network modules is proposed. The first module is a lane line detection model called UNET_CLB. Here, the sequence of image frames is utilised rather than only the current frame to deal with the missing lane lines. The second module is a lane‐following model called LSTM_DTS, which combines a deep learning attention mechanism (temporal attention network and spatial attention network) with a recurrent neural network. As a result, the proposed UNL Lane Follower produces smoother driving behaviour, especially when a lane line is temporally missed. For better explain ability, the role of each part of the network structure is analysed and explained intuitively. As a modularised network, the UNET_CLB is firstly trained and tested on the TuSimple dataset and CULane dataset. The LSTM_DTS lane follow is then trained and tested on our actual lane following dataset. Finally, the UNL Lane Follower is trained and tested as a whole in a simulation running on Webots, after importing the weight of the two modules trained separately. All testing results showed that the UNL Lane Follower can provide better robustness and accuracy for lane line following mission in the line missed situations.

Fast and Intelligent Ice Channel Recognition Based on Row Selection

The recognition of ice channels plays a crucial role in developing intelligent ship navigation systems in ice-covered waters. Navigating through ice channels with the assistance of icebreakers is a common operation for merchant ships. Maneuvering within such narrow channels presents a significant challenge for the captain’s skills and ship performance. Therefore, it becomes essential to explore methods for enabling ships to navigate through these channels automatically. A key step in achieving this is the accurate recognition and extraction of boundary lines on both sides of the ice channel. An ice channel line recognition method based on the lane line detection algorithm UFAST is implemented. The method is trained and tested on the constructed ice channel dataset, with the test results showing that the average recognition accuracy reaches 84.1% and the recognition speed reaches 138.3 frames per second, meeting the real-time requirement. In order to solve the current lack of authentic ice channel images, ice channel navigation scenes are built based on UE4, and synthetic ice channel images are rendered. The method in this paper is also compared with the traditional non-intelligent Otsu threshold segmentation method and the intelligent instance segmentation method YOLACT for performance analysis. The method in this paper has 9.5% higher ice channel recognition accuracy and 103.7 frames per second higher recognition speed compared with YOLACT. Furthermore, ablation studies are conducted to analyze the relationship between the number of gridding cells in the proposed method and ice channel recognition accuracy.