An Efficient Text Detection Model for Street Signs

Text detection in natural scenes is a current research hotspot. The Efficient and Accurate Scene Text (EAST) detector model has fast detection speed and good performance but is ineffective in detecting long text regions owing to its small receptive field. In this study, we built upon the EAST model by improving the bounding box’s shrinking algorithm to make the model more accurate in predicting short edges of text regions; altering the loss function from balanced cross-entropy to Focal loss; improving the model’s learning ability on hard, positive examples; and adding a feature enhancement module (FEM) to increase the receptive field of the EAST model and enhance its detection ability for long text regions. The improved EAST model achieved better detection results on both the ICDAR2015 dataset and the Street Sign Text Detection (SSTD) dataset proposed in this paper. The precision and F1 scores of the model also demonstrated advantages over other models on the ICDAR2015 dataset. A comparison of the text detection effects between the improved EAST model and the EAST model showed that the proposed FEM was more effective in increasing the EAST detector’s receptive field, which indicates that it can improve the detection of long text regions.

Clichés and pragmatemes

In order to properly classify the phraseme (that is, a constrained, or non-free, expression) No parking, a universal typology of lexical phrasemes is proposed. It is based on the following two parameters:• The nature of constraints— Lexemic phrasemes: the expression is constrained with respect to freely constructed meaning.— Semantic-lexemic phrasemes: the expression is constrained/non-constrained with respect to the meaning constrained by the conceptual representation.— Pragmatemes: the expression is constrained with respect to pragmatic conditions, that is, to the extralinguistic situation of its use (in a letter, on a street sign, on a package of perishable food).• The compositionalityThe expression can/cannot be represented as regular “sum” of its components.As a result, we have, firstly, the following major classes of lexical phrasemes:1) Non-compositional lexemic phrasemes: idioms (˹cold feet˺, ˹shoot the breeze˺)2) Compositional lexemic phrasemes: collocations (rain heavily, pay a visit)3) Non-compositional semantic-lexemic phrasemes: nominemes (Big Dipper, New South Wales)4) Compositional semantic-lexemic phrasemes: clichés (See you tomorrow! | Absence makes the heart grow fonder.)For clichés, the least-studied class of phrasemes, a more detailed classification is proposed (as a function of the type of their denotation). Secondly, each phraseme (except a nomineme) and each lexemes can be pragmatically constrained, i.e. a pragmateme: ˹Fall out!˺ (idiom; a military command) | Take aim! (collocation; a military command) | Emphasis mine/added (cliché; in a printed text) | Rest! (lexeme; a military command).

Visual Search: How Do We Find What We Are Looking For?

In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.

An Advanced Edge-Detection Method for Noncontact Structural Displacement Monitoring

A non-contact vision sensor system for monitoring structural displacements with advanced Zernike subpixel edge detection technique is suggested in this paper. Edge detection can detect features of objects effectively without using templates. Subpixel techniques provide more accurate and cost-effective results when compared to integer pixel methods. Built on these two techniques, a new version sensor method was developed to detect the vibrations of structures in this study. Satisfactory agreements were found between the displacements measured by the vision sensor system and those recorded by the Multipurpose Testing System (MTS). A field test was then carried out on a street sign using the proposed vision system. Satisfactory results were obtained using the new version of the sensor system at many points simultaneously without any manually marked targets. Moreover, the system was able to provide natural frequencies and mode shapes of the target instantaneously, which could be used to accurately locate damage.

Higway Criuse Control System for Vehicles using Low Power RF and Can Protocol

Nowadays mishaps are happening much of the time, causing destruction of numerous individuals by committing unassuming errors while driving (in school zone, slopes region, and roadways). In any case, once in a while it may not be conceivable to see the billboards put by the Highway Department to caution the drivers in such sort of spots and there is an opportunity for mishap. The headway in the processor innovation and microcontrollers has opened another framework intended to forestall the mishaps caused because of carelessness of drivers in observing rush hour gridlock flags close by the street and different abnormalities on the streets. So to suggest the driver about the zones and to consequently keep up the speed is cultivated by methods for low power RF innovation. The primary target is to plan an Electronic Display controller implied for vehicle's speed control and screens the zones, which runs on an implanted framework and can be hand crafted to fit into a vehicle's dashboard to show data on the vehicle. This framework whenever received by some state can successfully diminish the quantity of street mishaps brought about by speeding vehicles losing control of the vehicle at speed breakers or by driver's carelessness towards traffic signals. This paper presents another structure to control the speed of the vehicles at clumsy zones and security zone places for fixed time. The undertaking is made out of two separate units: Zone status transmitter unit, Electronic Display and Control unit. When the street sign is gotten from the zones, the vehicle's Electronic Display Controller Unit cautions the driver, to lessen the speed as indicated by the zone; it hangs tight for driver's reaction and diminishes the speed of vehicle consequently with CAN Protocol.

Text Detection in Natural Image By Connected Component Labeling

The process of detect the text from the natural image is complex and difficult process because the variance by the devises that take the images and different the texts that found in images in the orientation, size and style. Given the importance the texts in images in the several of application of computer vision. In this paper dependent on the spatial natural images and on the spatial data set for the street sign that include the texts by the different size and different orientation. In this paper detected the texts in images by using robust method by using several algorithms, at the first stage making preprocessing for the image to blur the image and reduce the nose on it by Gaussian blur, second stage making processing that include canny edge detection to detect the edges and dilation, third stage applying connected component to filling all objects in image then applying stroke width transform(SWT) to detect the letter candidate and applying the system on the several images that include different types of texts.

Evaluation of a Training Intervention to Improve Novice Drivers’ Hazard Mitigation When Approaching Left Turn Scenarios

Crash statistics reveal that newly licensed teenage drivers experience a higher risk of crashing than more experienced drivers, particularly when turning left across the path of approaching traffic. Research has also demonstrated that novice drivers exhibit poor hazard mitigation skills. The current study assesses the effectiveness of a training program aimed at improving novice drivers’ hazard mitigation and speed selection behaviors as both the through driver and turning driver in left turn across path scenarios. In this study, novice drivers were randomly assigned to one of two training cohorts: anticipation-control-terminate (ACT) or placebo. Phase 1 of ACT is a video game where drivers must select where to look, where they would steer, and when they would slow when observing the approach to known fatal crash risk scenarios. Placebo training involved reaction time tests and street sign definitions. In phase 2 the ACT-trained participants were shown where their choices were similar to, or different than, those of drivers aged 26 through 61who had not had crashed in the previous 10 years. In phase 3, ACT-trained drivers were compared with placebo-trained drivers at left turn scenarios both as through driver and turning driver, using a driving simulator. ACT-trained drivers were more likely to exhibit anticipatory glances and slowing behaviors, and were significantly less likely to crash than were placebo-trained drivers. The results indicate that ACT was effective as a countermeasure for training novice drivers to select better speed management strategies in the simulated scenarios utilized in this research.

Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks

Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera.