Automatic traffic sign detection and recognition: A review

Swathi, M.; Suresh, K. V.

doi:10.1109/icammaet.2017.8186650

Cited by 21 publications

(12 citation statements)

References 18 publications

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“…Figure 1 shows Driving Control Systems (DCS) and algorithms for a smart EV. DCS encompasses control methods and control modes based on ADAS [7], including Adaptive Cruise Control (ACC) [8], Automatic Emergency Braking System (AEBS) [9], Lane Departure Warning (LDW) [10], [11], Lane Change Assist (LCA) [12], Lane Keeping Assist (LKA) [13], Night Vision [14], Traffic Sign Recognition (TSR) [15], Pedestrian Detection [16], Automatic Parking [17], and Traction control [18]. Traffic flow parameters, driver behavior, and driving conditions can be detected and shared with vehicles within their vicinity.…”

Section: Introductionmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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Section: Introductionmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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“…TSDR systems have two main stages i.e. the first stage to detect and localize the traffic sign if they exist, and the second stage is to classify and recognize the detected traffic signs [8]. In the case of any false detection by the detection system, the performance of the overall TSDR system can be quite low because the classifiers of recognition stage are not usually trained on false alarms.…”

Section: Introductionmentioning

confidence: 99%

“…The dominant colors of the traffics signs are red, blue and finally yellow [7], so the color based detection is primarily focused on these colors. And, the major shapes of the traffics signs are triangle, rectangle, octagon and finally circle [8]. There are five basic traffic sign classes i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Traffic sign detection can be handled by different ways such as only color-based detection, only shape-based detection, both color and shape-based detection and finally learning based methods [8,7]. Using only color or shape based detection is not suitable because there exist a high risk of false alarms when the image or the video frame contains other objects having same colors and shapes with the traffic signs [8]. Therefore, first using the color detection and then engaging shape detection prevents any possible intervention of other objects [8].…”

Section: Introductionmentioning

confidence: 99%

“…Using only color or shape based detection is not suitable because there exist a high risk of false alarms when the image or the video frame contains other objects having same colors and shapes with the traffic signs [8]. Therefore, first using the color detection and then engaging shape detection prevents any possible intervention of other objects [8].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

Uluskan

2020

International Journal of Automotive Science and Technology

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In this paper, a new effective method for automatic detection of circular traffic signs is introduced. Automatic traffic sign recognition is a crucial application of Driver Assistance Systems for safe and comfortable driving conditions. The major step of traffic sign recognition is to detect and localize the traffic signs if they exist. An important portion of traffic signs (i.e. regulatory traffic signs) has circular shape. They include vital information about the traffic rules and regulations (especially the speed limits). Therefore, this study introduces an advanced circular detection method to detect and localize the circular traffic signs. In the previous literature, it is known that a circle creates a distinctive sinusoidal structure in Straight Line Hough Transform (SLHT). This study exploits this notion in circle detection by trying to catch a part of the envelopes of this sinusoidal structure. First, post edge detection is applied to SLHT, and this image is called H-Edge image. Then, edge linking is performed on H-Edge image to obtain multiple candidate curves. By sinusoid curve fitting and sinusoidal normalization, the curves belonging to the sinusoidal structure are identified, so the circle in the original image is detected. Furthermore, a new effective iterative linear image segmentation method which is based on local minima in SLHT is proposed. Combining these two methods and color filtering, a new effective method for circular traffic sign detection is obtained. For certain sample images, the new method effectively detects and localizes the existing circular traffic signs.

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Recognizing the Indian Cautionary Traffic Signs usingGAN, Improved MaskR‐CNN, and Grab Cut

Satti

Devi

Srinivasan

2022

Concurrency and Computation

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The detection and classification of traffic signs is a major challenge for self-driving vehicles. The task can be narrowed down to detecting and classifying Indian Cautionary Traffic Signs (ICTS). In this proposed work, the difficulty of detecting and identifying Indian Cautionary Traffic Signs is addressed, and sincere attempts have been made to attain a possible solution using a Generative Adversarial Network (GAN), Improved Mask R-CNN, and GrabCut algorithms. Data augmentation is done using Cascade Pyramid Generative Adversarial Network (CP-GAN) to upsize the data set. The Mask R-CNN with certain adoptions termed Improved mask RCNN is used in conjunction with the Grab-Cut method to handle ICTS detection and identification through automatic end-to-end learning. Initially, Improved Mask R-CNN generates a pixel-by-pixel segmentation mask for each item in the input sample. Masks developed using Improved Mask R-CNN are not always clean, that is, some background pixels are often seen in the foreground segmentation. Hence, the generated masks are refined using the Grab Cut algorithm to enhance image segmentation. This combined approach works well in isolating the traffic signs from the ground truth images. Improved Mask R-CNN attained better performance in the overall performance of traffic sign detection in the Indian data-set. This method recognizes 40 different types of cautionary traffic signs from the unique Indian data set. The results are provided for complicated traffic sign categories that have not been addressed before. The proposed technique is trained and evaluated on ICTS, GTSDB, STSD, LISA, and DITS data sets, and it is also compared against cutting-edge object recognition methods like Mask RCNN, Faster RCNN, SSD, and YOLOv3.

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Automatic traffic sign detection and recognition: A review

Cited by 21 publications

References 18 publications

A Review on Driving Control Issues for Smart Electric Vehicles

A Review on Driving Control Issues for Smart Electric Vehicles

Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

Recognizing the Indian Cautionary Traffic Signs usingGAN, Improved MaskR‐CNN, and Grab Cut

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