# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
###### Abstract  
Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions. pedestrian crossings; smart cities; computer vision; convolutional neural networks; performance evaluation +
Footnote †: journal: Medicalstate of these crossings, through these systems, can play a crucial role in improving urban infrastructure, contributing to safety and quality of life in cities. The principal objective of this project is to develop a prototype capable of detecting and classifying the deterioration of crosswalks. This prototype will generate data that can be accessed via a web platform designed for relevant authorities, thereby enabling them to ascertain the condition of various crosswalks and their respective states of disrepair. This paper is organized into five sections. Section 1 presents the context of this work and its objectives. Section 2 presents the computer vision techniques used to classify the state of degradation of pedestrian crossings. Two different approaches are considered, and the results obtained by each are evaluated. Section 3 presents the construction details of a functional prototype to detect and classify the state of degradation of pedestrian crossings. Section 4 presents the validation tests of the proposed prototype. Finally, Section 5 concludes this work and identifies potential future research developments. 2 Classification of the State of Degradation of Pedestrian Crossings with Computer Vision Techniques  
To achieve the main objective of the proposed work, the system developed should be able to detect and classify the state of degradation of crosswalks based on computer vision techniques. Computer vision is a field of study that focuses on the ability of computing machines to interpret images, videos, and other visual data. This information is used for a variety of purposes, including object detection and classification. Detection is related to identifying the location of the object, while classification is related to assigning a class to this object [2]. These concepts are illustrated in Figure 1.  
Cameras are typically the primary source of data for this purpose. It is therefore crucial to have a large data set to ensure accurate detection of the desired object. In addition, the implementation of algorithms is essential to allow the model to learn autonomously, without human intervention. This technology has applications in various fields, including machine learning [3] and deep learning [4]. These areas instruct computers to think like the human brain, enabling them to recognize complex patterns in images, texts, sounds, and other forms of data [5]. These patterns can be identified using convolutional neural networks (CNNs), which are a set of interconnected layers. The main purpose of these layers is to simulate the neurons of a human being. Figure 2 depicts the architectural framework of a convolutional neural network (CNN), which receives an image containing an object as input and returns one or more classes to classify the object's category.