scholarly journals Traffic Sign Detection Using Region And Corner Feature Extraction Method

2021 ◽  
Vol 3 (1) ◽  
pp. 21-24
Author(s):  
Hendra Maulana ◽  
Dhian Satria Yudha Kartika ◽  
Agung Mustika Riski ◽  
Afina Lina Nurlaili
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Safety Information ◽  
Extraction Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Analysis Process ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 661-668 ◽  
Author(s):  
Y. Li ◽  
J. Fan ◽  
Y. Huang ◽  
Z. Chen
Keyword(s):  
Geometric Constraints ◽  
Transportation Infrastructure ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Sign Detection ◽  
Mobile Mapping System ◽  
Transportation Agencies ◽  
Traffic Sign Detection

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kurniawan Nur Ramadhani ◽  
M.Syahrul Mubarok ◽  
Agnes Dirgahayu Palit
Keyword(s):  
Support Vector Machines ◽  
Road Safety ◽  
Support Vector ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Synthetic Traffic Signs Dataset for Traffic Sign Detection & Recognition In Distributed Smart Systems

2021 ◽  
Author(s):  
Ilias Siniosoglou ◽  
Panagiotis Sarigiannidis ◽  
Yannis Spyridis ◽  
Anish Khadka ◽  
Georgios Efstathopoulos ◽  
...  
Keyword(s):  
Traffic Sign ◽  
Traffic Signs ◽  
Smart Systems ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals REAL OBJECT DETECTION SYSTEM FOR IRAQ TRAFFIC SIGNS BASED ON MASK R-CNN

2021 ◽  
Vol 4 (3) ◽  
pp. 12-22
Author(s):  
Ammar A. Aggar ◽  
Mohammed J. Zaiter ◽  
Abdalrazak T. Raheem
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Timely Response ◽  
Sign Detection ◽  
Weak Light ◽  
Real Video ◽  
Important Object ◽  
Traffic Sign Detection

Traffic signs object detection has gained great interest in recent years, as one of the most important object detector applications. Traffic signs detection is based on deep learning, which gives it the benefit of high detection precision and timely response to condition changes of the traffic. Therefore, this paper shows an efficient method for detecting traffic signs in real-time. Hence, it implements a new Iraqi Traffic Sign Detection Benchmark (IQTSDB) dataset based on Mask Region-based Convolutional Neural Network (Mask R-CNN). The results show that the implementation of IQTSDB dataset with Mask R-CNN has a great efficiency in different conditions such as sunny, cloudy, weak light, and rainy conditions. In addition, the real video captured for traffic signs in Baghdad has been taken and compared to the German Traffic Signs Detection Benchmark (GTSDB) dataset. The IQTSDB dataset has a better performance than GTSDB dataset based on the performance parameters training loss and mean Average Precision (mAP).

scholarly journals Traffic Sign Detection and Recognition

2020 ◽  
Vol 5 (7) ◽  
pp. 1143-1146
Author(s):  
Anju C P ◽  
Andria Joy ◽  
Haritha Ashok ◽  
Joseph Ronald Pious ◽  
Livya George
Keyword(s):  
Language Processing ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Textual Data ◽  
Sign Detection ◽  
Local Residents ◽  
Non Local ◽  
Traffic Sign Detection ◽  
Detection And Recognition

As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The proposed system not only tackles the above-mentioned problem, but also to an extent ensuressafer driving by reducing accidents through conveying the traffic signs properly. The techniques usedto implement the system include digital image processing, natural language processing and machinelearning concepts. The implementation of the system includesthree major steps which are detection of traffic sign from a captured traffic scene, classification of traffic signs and finally conversion of classified traffic signs to audio message.

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