DEVELOPMENT OF A ROAD SIGN RECOGNITION SYSTEM BASED ON A SINGLE-STAGE DETECTOR
The detection of road signs is extremely important in autonomous driving systems and a generally influential aspect of road safety. To reduce the risk of accidents and improve the driving experience of drivers, road sign recognition systems have been developed, which have played an important role in automatic driving and maintenance of the road network. This article proposes a new model based on a convolutional neural network to improve the accuracy of detection and recognition of road signs, especially in conditions of poor visibility and extremely limited vision. The general model of the road sign recognition system consists of a number of interconnected modules. To increase the efficiency of calculations, a single-stage detector was proposed. The system consists of a main network, an auxiliary network and a detector. The main network extracts objects from the image and captures objects. With the initial output functions of the main network, the auxiliary network combines the functions and adapts the size, thereby improving the overall performance of the architecture. The detector receives three output signals from the network and provides a forecast of the position of the bounding box, the reliability of the object and object classes for each output layer of the object map. With high-quality feature maps of each layer, detection can be more accurate.