Signboard Detection Research Articles

Traffic Sign Detection Under Foggy Environment Using Machine Learning

Road signs are important to ensure smooth traffic flow without bottle necks or mishaps. Road symbols are the pictorial representations having different necessary information required to be understood by driver. Road signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. This paper presents an overview of the traffic sign board detection and recognition and implements a procedure to extract the road sign from a natural complex image, processes it and alerts the driver using voice command It is implemented in such a way that it acts as a boon to drivers to make easy decisions. Traffic sign recognition is a critical component of modern intelligent transportation systems, contributing to road safety and efficient traffic management. This paper presents a novel approach to traffic sign recognition based on Convolutional Neural Networks (CNNs). Key Words: CNN, Pre-processing, Feature Extraction.

Autonomous Road way Alert System

The "Autonomous Roadway Alert System" design process, which is based on the Waterfall Model for the System Development Life Cycle (SDLC), is discussed in this article. These sign board detection, traffic sign detection, obstacle detection, lane assistant and voice assistant features are the functions of the system tackled through phases of collection and analysis, design, implementation, integration, testing, deployment, and maintenance processes. Emphasis of use of Python-coded programs on Raspberry Pi hardware and YOLOv5 algorithm is given for the purpose of object identification.

Robust Signboard Detection and Recognition in Real Environments

The detection and recognition of signboards have become increasingly important in the consumer electronics industry due to its wide range of potential applications. These applications include aiding visually impaired consumers in navigating through unfamiliar areas, identifying location landmarks for wayfinding, and providing targeted advertising and marketing services to consumers. However, the accuracy of signboard detection remains challenging due to the diversity of designs, which may incorporate text and images, and the complexity of environments, such as occlusion, shooting angles, and lighting conditions. Most existing detection methods struggle to distinguish small and similar signboards. In this paper, we propose robust signboard detection and recognition based on template generation. We also collected a new dataset that contains about 30,000 images, in 14 categories of signboards in Taiwan for training and free public use. The proposed method is a one-stage detector, which utilizes multi-scale features in the Darknet-19 network to learn object features effectively, detecting tiny and large objects. In addition, the proposed template generation method was designed to improve the overall accuracy. We compare our results with the Yolo series models. The results show that our proposed method more efficiently detects and recognizes signboards, achieving an mAP score of 95.99%, total parameters of 62.7M, and FPS of 8.3.

Traffic Sign Detection

Abstract: Traffic signs are important to ensure smooth traffic flow without bottle necks or mishaps. Road Symbols are the pictorial representation having different necessary information required to be understood by driver. Road signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. This paper presents an overview of the traffic sign board detection and recognition and implements a procedure to extract the road sign from a natural complex image, processes it and it alerts the driver using text or voice command. It is implemented in such a way that it acts as a boon to drivers to make easy decisions.

Traffic Sign Board Detection and Voice Alert System

Automated tasks have simplified practically everything we perform in today's world. Drivers often miss signage on the side of the road in an effort to focus on the road, which can be dangerous for them and others. This issue may be avoided if there was a quick way to alert the driver without requiring them to divert their attention. TSDR (Traffic Sign Detection and Recognition) is useful in this situation because it detects and recognizes signs, alerting the motorist to any forthcoming signs. This not only assures road safety, but also puts the driver at ease when driving on unfamiliar or difficult roads. Another issue that arises frequently is the inability to comprehend the sign's meaning. Drivers will no longer struggle to grasp what the sign is saying thanks to this Advanced Driver Assistance Systems (ADAS) application. In this paper, we propose a method for detecting and recognizing traffic signs that uses image processing for detection and an ensemble of Convolutional Neural Networks (LeNet) for recognition. Because LeNet CNN have a high recognition rate, they are ideal for a variety of computer vision tasks. For the CNN implementation, Tensor Flow is employed. And here for object detection we implement yolo frame work On the German data sets, we achieved recognition accuracies of more than 99 percent for circular signs.

Automatic signboard detection and localization in densely populated developing cities

Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city streets. Yet, developing accurate signboard localization system is still an unresolved challenge because of its diverse appearances that include textual images and perplexing backgrounds. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. We use Faster R-CNN based localization by incorporating two specialized pretraining methods and a run time efficient hyperparameter value selection algorithm. We have taken an incremental approach in reaching our final proposed method through detailed evaluation and comparison with baselines using our constructed SVSO (Street View Signboard Objects) signboard dataset containing signboard natural scene images of six developing countries. We demonstrate state-of-the-art performance of our proposed method on both SVSO dataset and Open Image Dataset. Our proposed method can detect signboards accurately (even if the images contain multiple signboards with diverse shapes and colors in a noisy background) achieving 0.90 mAP (mean average precision) score on SVSO independent test set. Our implementation is available at: https://cutt.ly/dhr1D2K.

Traffic Sign Board Detection Using Single Shot Detection (SSD)

Abstract: Traffic sign board detection (TSBD) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signals more accurately and effectively can improve safe driving .Due to increase in technology there are autonomous vehicles . The traffic sign recognition process includes two parts: detection and classification. In this paper, we use an object detection algorithm called SSD to detect the traffic signs. This convolutional neural network uses multiple feature maps to detect objects. For the traffic sign is very small to the whole picture, the SSD model has been improved to have a better detection result of traffic signs. In the experiments, the model has been simplified and the size of the prior box has been modified. The improved network has a good detection effect on small targets. The results on the test data set show that the proposed algorithm performs well for single-target, multi-target and dark-light images. The precision and recall on the test data set are 91.09%, and 88.06%. Keywords: Automatic traffic sign board Detection, SSD, Image processing, Text alert.

Traffic Sign Detection

Road signs are important to ensure smooth traffic flow without bottlenecks or mishaps. Road symbols are the pictorial representations having different necessary information required to be understood by driver. Road signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. This paper presents an overview of the traffic sign board detection and recognition and implements a procedure to extract the road sign from a natural complex image, processes it and alerts the driver using voice command It is implemented in such a way that it acts as a boon to drivers to make easy decisions. There are several major challenges that affects the detection and recognition process of traffic signs and makes it difficult for the driver to identify the signs in adverse weather conditions and darkness, these challenges and problems are highlighted in this study. Traffic signs are detected based on various features such as color, shape, and texture etc. Based on these features numerous methods exists for detection of traffic signs. We have describe a new, real-time traffic sign detection. This challenge get more difficult to meet in a city like environment where multiple traffic signs, ads, parking vehicles, pedestrians, and other moving or background objects make the recognition much more difficult.

Reading signboards for the visually impaired using Pseudo-Zernike Moments

It is necessary to provide essential daily support for the visually impaired, allowing them to locate themselves in an underground station, to change trains, to read signboards like those in doctors offices, law firms, shops, etc. In this paper, we present a Reading Signboards System (called RSbS) based on Pseudo Zernike Moments (PZMs) and stroke features from a video captured by a camera. The system is made up of three main steps: (i) signboard detection, (ii) text localization and (iii) filtering of non-textual regions. In a video, signboard region contains all the necessary information. Therefore, the signboard region should be located and segmented from the other parts of a frame that will be considered as the background. Signboards localization and segmentation are performed using Pseudo Zernike Moments. Text localization and extraction is done using PZMs segmentation which is based on the HSV color space and specifically on the Value channel. Non-textual regions will be filtered out using the outline function. System evaluation shows that it is robust to changes in lighting, low resolution and camera tilt, and it gives very few false positives compared to other approaches. In addition, the resulting images are transmitted to an optical character recognition engine (the best known is OCR) to extract the text which will be converted to speech using one of the existing APIs like Google Text to Speech API (gTTS API).

Urdu signboard detection and recognition using deep learning

Urdu signboard detection and recognition using deep learning

A New Area based Algorithm for Traffic Sign Board Detection and Classification by SVM Approach

A New Area based Algorithm for Traffic Sign Board Detection and Classification by SVM Approach

RF Based Sign Board Detection and Collision Avoidance System

Nowadays the percentage of accidents has been going up day by day. This might be because of driver negligence of not following the traffic rules and sign boards. RF based sign board detection and collision avoidance system is such approach where the rider of the vehicle will get advance intimation of fore coming hurdles. Also, it detects front vehicle distance. This system is very helpful in cloudy area, curved roads. At night it is very difficult for driver to see the sign boards, answer for these issues is the ‘RF based sign board detection and collision avoidance system.

Real Time Traffic Signboard Detection and Recognition from Street Level Imagery for Smart Vehicle

The text or symbol detection and recognition from traffic panels is a challenging problem. Number of important application areas is dependent on text detection and recognition, including advanced driver assistance systems, road surveying, and autonomous vehicles. In this research project a novel system for the automatic detection and recognition of text and symbol in traffic signs is proposed. Search regions with in the image must be defined. In this particular region locate a large number of candidates, which are then reduced by applying constraints based on temporal and structural information. This problem can be divided in two stages; First stage will be detection of region and second will be character recognition. The detection stage exploits knowledge of the structure of the scene, the size and location of the road in the frame. Once a potential traffic panels has been located, the next stage attempts to recognize text and symbols within the region. For the purpose of text detection MSER is used and for recognition purpose optical Character Recognition method is used. Automatic testing using XML files provide better accuracy.