Symbolic road marking recognition using convolutional neural networks

Ahmad, Touqeer; Ilstrup, David; Emami, Ebrahim; Bebis, George

doi:10.1109/ivs.2017.7995910

Cited by 24 publications

(17 citation statements)

References 27 publications

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“…Similarly, a study on road-marking recognition using a convolutional neural network was reported by Ahmad et al [35]. The detection of the symbolic road markings was not within their scope of the study.…”

Section: Introductionmentioning

confidence: 80%

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

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Section: Introductionmentioning

confidence: 80%

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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“…Most image-based road marking detections use a deep learning method [43,44]. Because we need the 3D spatial information of the viewpoints on the roads, 2D image based road marking detection is not suitable in this paper.…”

Section: Road Marking Detection and Classificationmentioning

confidence: 99%

Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

et al. 2019

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Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

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“…Furthermore, our work is also related to data augmentation. [2] first utilized a data augmentation method of road marking. Different from the above method, our framework designs and trains a generative model to do data augmentation, it can be applied to even small and unconstrained data.…”

Section: Related Workmentioning

confidence: 99%

Unconstrained Road Marking Recognition with Generative Adversarial Networks

Lee

Hong

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates highquality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.

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Symbolic road marking recognition using convolutional neural networks

Cited by 24 publications

References 27 publications

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

Unconstrained Road Marking Recognition with Generative Adversarial Networks

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