The Verification of the Correct Visibility of Horizontal Road Signs Using Deep Learning and Computer Vision

Abstract

This research aimed to develop a system for classifying horizontal road signs as correct or with poor visibility. In Poland, road markings are applied by using a specialized white, reflective paint and require periodic repainting. Our developed system is designed to assist in the decision-making process regarding the need for repainting. It operates by analyzing images captured by a standard car camera or driving recorder. The image data undergo initial segmentation and classification processes, facilitated by the utilization of the YOLOv4-Tiny neural network model. The input data to the network consist of frames extracted from the video stream. To train the model, we established our proprietary database, which comprises 6250 annotated images and video frames captured during driving. The annotations provide detailed information about object types, their locations within the image, and their sizes. The trained neural network model effectively identifies and classifies objects within our dataset. Subsequently, based on the classification results, the identified image fragments are subjected to further analysis. The analysis relies on assessing pixel-level contrasts within the images. Notably, the road surface is intentionally designed to be dark, while road signs exhibit relatively lighter colors. In conclusion, the developed system serves the purpose of determining the correctness or visibility quality of horizontal road signs. It achieves this by leveraging computer vision techniques, deep learning with YOLOv4-Tiny, and a meticulously curated database. Ultimately, the system provides valuable information regarding the condition of specific horizontal road signs, aiding in the decision-making process regarding potential repainting needs.

Keywords

• road sign classification
• YOLOv4-Tiny
• image analysis
• deep learning