A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks

Mamun, Abdullah Al; Ping, Em Poh; Hossen, J.; Tahabilder, Anik; Jahan, Busrat

doi:10.3390/s22197682

Cited by 11 publications

(3 citation statements)

References 91 publications

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“…Consequently, evaluation characteristics using machine vision of current lane markings can also be examined by the same set of defined pavement markings factors (i.e., retro-reflectivity, weather, and luminance) (Stacy, 2019). Additionally, particular performance measures from image processing techniques can also be adopted for evaluating results such as precision, recall, true/false positives have also been adopted (Mamun et al, 2022).…”

Section: Lane Marking Impact Evaluationmentioning

confidence: 99%

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Formosa,

Quddus,

Man

et al. 2024

J. Transp. Eng., Part A: Systems

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The quality of lane markings is pivotal for safe operations and efficient trajectory generations of Connected and Autonomous Vehicles (AVs). However, most studies are devoted to enhancing invehicle detection systems and ignore the impact of faulty lane markings. An instrumented vehicle was employed to mimic the data input of an AV and real-world trials were conducted on (1) live motorways and (2) a controlled motorway facility. From the live motorway data, causal factors affecting computer vision lane detection and classification algorithms were examined and an enhanced lane classification algorithm was developed to overcome the limitations posed by poor lane markings. In the controlled motorway facility, experiments to modify the physical appearance of the lane markings were conducted to further test the performance of the developed algorithm. The detection rates of the developed lane classification algorithm were compared with the Lane Departure Warning (LDW) system already implemented in the vehicle. Findings revealed that the LDW system is accurate over 95% and 54% of time when lanes are faded by 50% and 75% respectively. Further testing on the quality of the lane markings was carried out virtually in such a way that the experiments were replicated in a simulation environment to: (1) identify lane marking conditions that can be reliably adopted for safe operations of AVs, (2) estimate the effect of adverse weather and lighting conditions on road markings detection and(3) address localisation issues for AVs. Simulation results show that poor lane markings have a significant negative impact on AV safety, especially in inclement weather and poor light conditions inducing an increase in conflicts, and delays. This can be compensated if more sophisticated sensors are employed in AVs, and the operators of road network develop lane-based digital road maps.

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Section: Lane Marking Impact Evaluationmentioning

confidence: 99%

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Formosa,

Quddus,

Man

et al. 2024

J. Transp. Eng., Part A: Systems

View full text Add to dashboard Cite

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“…DL-based lane detection methods, along with their advantages and limitations, were discussed in [ 197 ], while [ 198 ] presented an overview of lane detection and tracking algorithms for driver assistance systems, along with the tools/datasets used, performance evaluations, their advantages and disadvantages, problems encountered, patented products, and a SWOT (strengths, weaknesses, opportunities, and threats) analysis of different approaches. Similarly, [ 199 ] comprehensively examined research into lane marking with DNNs and DL techniques, explaining their successes and their limitations. Studies reveal that some challenges still remain that need further investigation, such as computational complexity, lack of generalization, and real-time computing in autonomous vehicles and mobile devices.…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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“…Aiming at the shortcomings of traditional lane line detection methods, the deep neural network (DNN) has a powerful deep cascade structure [39]. It extracts the hidden features of images through massive neurons, and uses the loss function as the target to update the network weight, so as to complete the lane line detection task in complex scenes.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning

Mao

et al. 2023

Sensors

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Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.

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A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks

Cited by 11 publications

References 91 publications

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning

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