Kernel Based Automatic Traffic Sign Detection and Recognition Using SVM

Gudigar, Anjan; Jagadale, Basavaraj N; Mahesh, P.K.; Raghavendra, U.

doi:10.1007/978-3-642-32112-2_19

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…These networks consist of neurons with learnable (adjustable) weights and biases [15]. Each neuron receives several inputs and then calculates the product of the weights multiplied by the inputs and finally presents a result using a nonlinear conversion (activation) function [16][17][18][19][20][21][22]. The entire network also provides a derivative score function, with raw input image pixels on one side and points for each category on the other.…”

Section: A Torsional Neural Networkmentioning

confidence: 99%

A Novel Method for Recognizing Traffic Signs using Color and Texture Properties using the ELM Algorithm

Cao¹

2022

IJACSA

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Road accidents cause a lot of financial and human losses every year. One of the causes of these accidents is human error, and the driver ignores traffic signs. Therefore, accurate detection of these signs will help to increase the safety of drivers and pedestrians and reduce accidents. In recent years, much research has been done to increase the accuracy of panel recognition, most of which are problems that affect the diagnosis, such as adverse weather conditions, light reflection, and complex backgrounds. In the present study, considering the diversity of traffic signs' geometric shapes, the ngis detection part has been done using a torsional neural network. Then, in the feature extraction section, we used LBP and HOG techniques, and at the end, the section was identified and classified using the ELM algorithm. The results obtained on 12569 images, 75% of which were used for training and 25% for experimentation, show that the accuracy of this research has improved by 95% compared to the essential work by 93%.

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Section: A Torsional Neural Networkmentioning

confidence: 99%

A Novel Method for Recognizing Traffic Signs using Color and Texture Properties using the ELM Algorithm

Cao¹

2022

IJACSA

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“…The method described in this paper will significantly improve the efficiency and detection rate of traffic signs, while also reducing the detection system's hardware specifications. The authors of [12] use the Radial Symmetry Transform to identify other geometric shapes such as octagons, squares, and triangles.…”

Section: Related Workmentioning

confidence: 99%

Road Sign Detection Using Variants of YOLO and R-CNN: An Analysis from the Perspective of Bangladesh

Lima

Kabir

Das

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Road sign detection represents a feature that assures the safety of drivers, vehicles, and pedestrians by efficiently detecting road signs. This feature is designed to notify drivers about road signs whether he is missing the signs or not. This detecting and recognizing feature of the road signs' has improved a part of the advanced driver assistance system (ADAS). ADAS is an automated technology containing cameras and sensors intended to help the drivers with road signs, while traveling to a new road or having no knowledge about road signs. Before the work analysis, this topic has shown formidability as it has a real-time processing solution. This paper analyzed seven architectures for detecting the road signs: YOLO, YOLOv2, YOLOv3, PP-YOLO model and R-CNN, Fast R-CNN, Faster R-CNN. We have built a dataset based on Bangladesh's road sign named the "BD Road Sign 2021 (BDRS 2021)" dataset to evaluate the architectures. This dataset contains 16 categories (16 types of road-sign), and each has 168 images. Finally, we applied the seven advanced architectures to find the effective one to detect Bangladesh's road signs. This study implies that YOLOv3 and Faster R-CNN perform comparatively better for road sign detection.

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“…In another paper [2], detection and identification of road signs are an important matter in recent research. Traffic lights and road signs have been constructed in compliance with strict guidelines using special colors and patterns that are very distinct from the surroundings.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Traffic Sign Recognition System using Computer Vision

Prabu*,

Patil,

Yadav

et al. 2019

IJITEE

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There are many existing companies who are developing cars on the autonomous driving technology. With the help of GPS and internet connectivity they create a dynamic map which helps the cars to navigate. This technology is still new and undergoing rigorous changes. There are many shortcomings to this existing technology. They are capable of navigating through those areas which are accounted for and surveyed but when the car enters in any unchartered terrain or there is any internet connectivity issues, the updation in the map is not possible, which leaves the car to navigate on its own. This can cause many troubles like you can get late or maybe lost. So to overcome these problems we need such an intelligent system with the help of camera feeds can monitor and identify the traffic signals dynamically. Traffic sign recognition is based on Advanced Driving Assistance System (ADAS) which is used by vehicles to recognise various traffic signs ahead. The system takes continuous video input from the dashboard camera or the camera mounted on the bonnet of the car. The underlying algorithm extracts the features of the input image and matches them with an existing library of traffic sign. The output is fed to the driving assistance system and it in turn drives the car accordingly. This intelligent system uses computer vision. This device will take camera feeds and upgrade the ADA system instantaneously. The algorithm has been implemented using Python language.

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Kernel Based Automatic Traffic Sign Detection and Recognition Using SVM

Cited by 14 publications

References 7 publications

A Novel Method for Recognizing Traffic Signs using Color and Texture Properties using the ELM Algorithm

A Novel Method for Recognizing Traffic Signs using Color and Texture Properties using the ELM Algorithm

Road Sign Detection Using Variants of YOLO and R-CNN: An Analysis from the Perspective of Bangladesh

Automated Traffic Sign Recognition System using Computer Vision

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