This paper describes a traffic sign detection framework for greyscale images. The system is a heterogeneous cascade classifier formed by a rectangle features cascade followed by specific filters for each shape. We define two types of filters: The first one is based on local changes of the gradient direction and the second one is based on the idea of radial symmetry and gives us the centre of ci...

A vision-based vehicle guidance system for road vehicles can have three main roles: 1) road detection; 2) obstacle detection; and 3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easie...

Traffic sign detection is an important research direction in the process of intelligent transportation Internet era, and plays a crucial role ensuring traffic safety. The purpose this to propose traffic-sign-detection algorithm based on selective kernel attention (SK attention), explicit visual center (EVC), YOLOv5 model address problems small targets, incomplete detection, insufficient accurac...

“Machine Learning (ML) is the subfield of Artificial Intelligence conceived with the bold objective to develop computational methods that would implement various forms of learning, in particular mechanisms capable of inducing knowledge form examples or data” (Kubat, Bratko & Michalski, 1998, p. 3). The simplest and best-understood ML task is known as supervised learning. In supervised learning,...

Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of cod...

In this paper we present an approach to the detection and extraction of text in road sign panels. Text strings, indicators and signs extraction is efficiently performed so OCR algorithms can recognize different characters that may be present on the traffic plane. In a first step, basic color segmentation and shape classification is done for the purpose of detecting possible rectangular planes. ...

In classification problem, the most important factor is training dataset which is effect accuracy rate of classification. However, we encounter with imbalanced data set in real-world applications. In this dataset, the number of images in some classes is rather less than the number of images in other classes. So estimation of classification is tent to majority class and minority classes will be ...

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate currently QNN do not outperform classical DCNN (Deep Convo...