Image processing based traffic sign detection and recognition with fuzzy integral

Taştimur, Canan; Karaköse, Mehmet; Çelik, Yavuz; Akın, Erhan

doi:10.1109/iwssip.2016.7502715

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…After rail track lines are detected by using Hough transform [28,29], the intersection of two straight rail lines in the image can be computed. To compute vanishing point, common form of a linear equation can be used.…”

Section: Switch Detection Methodsmentioning

confidence: 99%

“…XYZ color space is expressed such that all visible colors can be defined using only positive values. And the Y value is luminance [29]. Red, green, and blue values are to be unwanted for creating a standardized color model that is suitable for all devices [29].…”

Section: Level Crossing Detection Methodsmentioning

confidence: 99%

“…And the Y value is luminance [29]. Red, green, and blue values are to be unwanted for creating a standardized color model that is suitable for all devices [29]. Because of this reason XYZ is used a mathematical formula to convert the RGB data.…”

Section: Level Crossing Detection Methodsmentioning

confidence: 99%

“…The connected components have been detected to extract rail track from the railway image. After rail track is labelled on image, rail lines have been founded by utilizing Hough transform [28,29]. Hough transform returns lines that have different lengths.…”

Section: = + (3)mentioning

confidence: 99%

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A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

Taştimur

Karaköse

Akın

2016

International Journal of Applied Mathematics, Electronics and Computers

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Rail track, turnout and level crossing are critical parts of railway components. So faults of rail surface are directly related to the operation safety. The defects occurring in switches can lead to accidents and the derailment of the train. Therefore, it is necessary to utilize advanced technology to monitor rail damage of the switch and level crossing zones. In this study, the switch and level crossing detection are performed with vision based contactless image processing techniques. Edge detection algorithm, image processing filters, morphological feature extraction, Hough transform and hierarchical SVM classifier are utilized to detect turnout and level crossing regions in the railway images. While switch points are detected, images of the area in front of the train are continuously taken from camera placed on the train. These images contain foreign objects such as buildings, trees, etc. The foreign objects existing in the railway image make it difficult to detect accurately the rail track. The turnout and level crossing detection cannot also be performed using the information of the rail track that cannot be correctly detected. The several processes, such as some image processing filters and morphological feature extraction, have been performed to solve this problem. With destroying the foreign objects from the railway images, both the accuracy rate of the proposed method has been increased and real-time implementation of the method is provided. The Hough transform is utilized to detect rail track, switch and level crossing in the proposed method. The turnout detection is performed by applying SVM classifier to railway image. The proposed method provides real-time rail track, level crossing and turnout detection. Furthermore, this method has gained dynamic feature due to not be utilized any template image for switch detection. When evaluated from these aspects, both the proposed method is superior to other methods in literature and results were obtained in a shorter time thanks to applying ROI Segmentation to the railway image.

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Section: Switch Detection Methodsmentioning

confidence: 99%

Section: Level Crossing Detection Methodsmentioning

confidence: 99%

Section: Level Crossing Detection Methodsmentioning

confidence: 99%

Section: = + (3)mentioning

confidence: 99%

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A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

Taştimur

Karaköse

Akın

2016

International Journal of Applied Mathematics, Electronics and Computers

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“…Therefore, an efficient algorithm is required to perform the processing and analysis of the captured image. Several machine learning algorithm has been proposed for traffic sign recognition include the TensorFlow transfer learning algorithm [8]- [10], AdaBoost algorithm [11], convolutional neural networks (CCN) algorithm [12], [13], fuzzy integral algorithm [14], neural network [15], artificial neural network (ANN) [16], deep learning [17], [18], color transformation [19], and texture feature extraction [20]. The types of algorithms used inside TensorFlow, such as transfer learning, increase the efficiency of the traffic sign recognition when compared to traditional machine learning.…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic sign detection and recognition using Raspberry Pi

Isa

Yeong

Azyze

2022

IJECE

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Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

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Brazilian Traffic Signs Detection and Recognition in Videos Using CLAHE, HOG Feature Extraction and SVM Cascade Classifier with Temporal Coherence

Junges

Paula

Aguiar

2019

Advances in Soft Computing

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Image processing based traffic sign detection and recognition with fuzzy integral

Cited by 14 publications

References 19 publications

A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

Real-time traffic sign detection and recognition using Raspberry Pi

Brazilian Traffic Signs Detection and Recognition in Videos Using CLAHE, HOG Feature Extraction and SVM Cascade Classifier with Temporal Coherence

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