Robust chinese traffic sign detection and recognition with deep convolutional neural network

Qian, Rongqiang; Zhang, Bailing; Yue, Yong; Wang, Zhao; Coenen, Frans

doi:10.1109/icnc.2015.7378092

Cited by 43 publications

(16 citation statements)

References 20 publications

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“…Variants of CNN networks are widely used in CV studies in the field of ITS. There are a number of CNN-based studies in the literature, such as those focused on automatic license plate recognition [ 40 , 41 ], traffic sign detection and recognition [ 25 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], vehicle detection [ 52 , 53 , 54 , 55 ], pedestrian detection [ 56 , 57 , 58 , 59 , 60 ], lane line detection [ 61 , 62 , 63 ], obstacle detection [ 64 ], video anomaly detection [ 65 , 66 , 67 , 68 ], structural damage detection [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], and steering angle detection [ 79 , 80 , 81 , 82 ] in autonomous vehicles. The most popular and advanced CNN-based architectures in the literature [ 83 , 84 ] are presented in Figure 3 .…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…Traffic sign detection, which is a similar task, involves identifying the region of the image that contains a traffic sign. The accuracy of traffic sign detection is measured in terms of mAP; moreover, to determine whether a detected region is correct, the intersection over union value (IoU) is calculated and compared with a threshold value, usually set to 0.5 [ 47 ].…”

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

confidence: 99%

“…Traffic sign detection and traffic sign recognition were handled together by Qian et al [ 47 ]. First, using the R-CNN variant, potential traffic sign regions in the images were determined by means of RGB space thresholding; subsequently, the traffic sign recognition process was carried out using the CNN model.…”

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

confidence: 99%

See 2 more Smart Citations

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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Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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“…Regarding classification, deep learning is the method with the best performance in recent years [14]. Among the deep learning models, the convolutional neural networks (CNN) are the most popular techniques [15].…”

Section: Traffic Sign Recognitionmentioning

confidence: 99%

Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads

Iparraguirre

Amundarain

Brazalez

et al. 2021

Sensors

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European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.

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“…CNNs have recently become the de facto standard for computer vision and pattern recognition as they achieved the state-of-the-art performances in challenging tasks such as handwriting recognition, classification of large image archives, and face segmentation. In the context of traffic engineering, successful applications of CNN have been reported including flow speed prediction [15], traffic density measurement [16,17], pavement distress detection [18], road crack detection [19], and detection of traffic signs [20][21][22][23] or pedestrians [24,25]. 2.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020

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This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

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Robust chinese traffic sign detection and recognition with deep convolutional neural network

Cited by 43 publications

References 20 publications

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

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