Real-time traffic sign detection and recognition using Raspberry Pi

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.

Finely Crafted Features for Traffic Sign Recognition

Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System (ADAS) and intelligent automobile, whileas high-qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become an active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.

Violation of the use of traffic regulation means as a cause of a traffic accident

This article provides statistical data on the number of road accidents and their consequences (deaths and/or injuries) in all regions of Ukraine in 2021 as compared to the same period in 2020. Regions with a decrease in road traffic accident rates (Zakarpattya region, 9.6% decrease) and their percentage increase (Zhytomyr region, 56.1% increase) are highlighted. Based on the analysis of statistical data, it is estimated that the number of road traffic accidents in the country during this period increased by 19.6%. But as a positive phenomenon, the number of injuries and/or fatalities in these road accidents decreased by 17.2%. The article analyses and gives concrete examples of the use of some legally adopted road traffic control devices in the regional center of Ukraine - Zhytomyr city (traffic lights, road signs and road markings). Shows incidents of their use which are characterized by violations of traffic rules (Sections 8.1., 8.2, 8.3, 8.4, 8.7, Sections 33 "Traffic signs" and 34 "Traffic lanes"), DSTU 2587:2021 "Traffic lanes. General Technical Conditions", DSTU 4100:2021 "Road Safety. Road Signs". At the same time there are situations when road signs are in contradiction, which is categorically unacceptable. It also shows the cases where traffic signs are installed in a shape and design that is not included in traffic regulations and the relevant standard. It was concluded that the use of the above road traffic control devices in violation of the conditions of their installation (application) can lead to misinformation of road users, create additional informational and emotional pressure on them, contribute to increased fatigue, which consequently increases the probability of occurrence of road accidents and increases their importance.

Research on the law of reflection coefficient of reflective film of traffic sign based on Angle

In this paper, a large number of digital printing reflective film retroreflectivity measurement. Based on the multi-angle test of the reflective film of the mainstream manufacturers in the market, the reverse reflection coefficient of the digital printing reflective film was obtained. Through the curve fitting of the measured values of the backreflection coefficient under different measuring angles by using the scatter plot, the variation law of the luminosity of the digital printing reflective film with incident Angle and observation Angle was obtained. The variation law of backreflection coefficient explored in this paper has certain significance to the application guidance of digital printing reflective film for traffic signs.

THE RELATIONSHIP BETWEEN TRAFFIC SIGN COMPREHENSION AND RISKY TRAFFIC BEHAVIORS

The current study investigates the relationship between risky traffic behaviors and traffic sign comprehension (TSC). It is hypothesized that, as traffic sign comprehension increases, unsafe traffic behaviors decrease. The data were collected online through Qualtrics from 275 participants, 177 of whom were drivers. The questionnaire package included 25 open-ended traffic sign questions, Pedestrian Behavior Scale, Mini-Driver Behavior Questionnaire with 3 additional aggressive violation items, and a demographic information form. The results indicated that TSC was significantly related to reported driver errors and lapses after controlling for age and gender. In addition, pedestrian-related TSC was significantly related to reported pedestrian transgressions, lapses, aggressive behaviors, and positive behaviors after controlling for age, gender, driver's license, and driving experience. Generally, the results were consistent with the expectations: the better that road users (drivers and pedestrians) understand traffic signs, the fewer drivers and pedestrians reported unsafe behaviors (errors and lapses for drivers; transgressions, aggressive behaviors, and lapses for pedestrians), and the more pedestrians reported positive behaviors. This finding can be explained by the fact that as the need of people to understand traffic signs increases, they avoid behaviors that will lead to accidents in traffic. For this reason, it can be predicted that comprehensively introducing children to traffic signs from an early age will contribute positively to road safety.

Traffic Sign Detection and Recognition using Image Processing

Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

Traffic Sign Detection Using Region And Corner Feature Extraction Method

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.

Equipment Condition for Zebra Crossing Night-Time Safety Performance in Latvia

According to road traffic accident (hereinafter referred to as RTA) statistics, the vulnerable road users are pedestrians in Latvia. The aim of this study is to investigate and analyse technical equipment used on non-signalled pedestrian crossings (zebra crossings) in Latvia and to make suggestions for measures that would increase road traffic safety on zebra crossings. RTAs involving collisions with pedestrians were filtered from the Ministry of the Interior database for a three-year period from 2016 to 2018. Thirty-two zebra crossings with a higher number of accidents with pedestrians were observed on the spot during the daylight and at night in several cities of Latvia. The main emphasis during the observation was placed on traffic signs and zebra road marking performance. Pedestrian crossings were observed from car driver’s view by taking photographs during day-time and night-time observations. Most attention was paid to road sign and road marking visibility from driver’s seat position. Retroreflection coefficient R’ was measured for each pedestrian crossing road sign. It was found that the condition and performance of traffic organisation equipment were not maintained on a regular basis and the life cycle of some traffic signs had well expired. Many road signs do not comply with minimum requirements, and road markings have weak visibility during wet weather conditions. It is recommended to improve visibility of pedestrian crossings from driver’s view in the urban areas by increasing rain vision for road markings and higher retroreflection class for traffic signs.