Scholarlink

Roadway infrastructure forms the backbone of the social and economic growth of an area, but potholes adversely affect road condition, causing accidents and damage to vehicles. As a solution to the problem, we introduce “Fix My Way,” a web application based on machine learning to identify and authenticate potholes on roads in Madhya Pradesh, India. The front end, developed using HTML, CSS, and JavaScript, enables the user to provide complaints with images, location, contact information, and landmarks. Images are scanned through the YOLOv8 model(Instance segmentation), It is labeled Kaggle dataset through Roboflow, to identify potholes. Only validated complaints are inserted into a PostgreSQL database. Interaction between the front end, ML model, and database is achieved through a backend using Flask. The system contains a complaint page to ensure transparency and enable tracking of submissions by users. Through image validation and geolocation enabling effective, priority-based road maintenance and intelligent infrastructure monitoring.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

“Fix My Way” Using YOLOv8 and Machine Learning

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Prasad Enagandula,
  • Nikhil Vangaveti,
  • T. Parvath Reddy,
  • Vangaveti shivasaketh,
  • Charan Teja Karanam

摘要

Roadway infrastructure forms the backbone of the social and economic growth of an area, but potholes adversely affect road condition, causing accidents and damage to vehicles. As a solution to the problem, we introduce “Fix My Way,” a web application based on machine learning to identify and authenticate potholes on roads in Madhya Pradesh, India. The front end, developed using HTML, CSS, and JavaScript, enables the user to provide complaints with images, location, contact information, and landmarks. Images are scanned through the YOLOv8 model(Instance segmentation), It is labeled Kaggle dataset through Roboflow, to identify potholes. Only validated complaints are inserted into a PostgreSQL database. Interaction between the front end, ML model, and database is achieved through a backend using Flask. The system contains a complaint page to ensure transparency and enable tracking of submissions by users. Through image validation and geolocation enabling effective, priority-based road maintenance and intelligent infrastructure monitoring.