This project leverages YOLO (You Only Look Once) to detect various traffic violations in real-time, aimed at improving road safety and compliance.

This project basically aims to speed up the ticketing process for Traffic Sergeants by using their mobile phones and issue tickets by taking a couple of photos and uses Computer Vision APIs to process information from those photos.

Annotated image dataset for detecting wrong-turn traffic violations at roundabouts, supporting research in autonomous vehicles, intelligent transportation systems, and computer vision.

Web (Django) application and Python appplication to keep track of traffic violations through RFID System in Raspberry Pi 3B. Hosted at: https://trafficpenaltysystemsk17.azurewebsites.net/ . Corresponding IoT/Raspberry Pi repository at: https://github.com/shashwatkathuria/IoT-TrafficMonitoringSystem

Developed a full-stack traffic system with FastAPI, Flask, React, and MySQL. It manages vehicle records and violations, and uses an AI model to detect no-helmet cases from images and automatically log them with evidence.

Automated detection of vehicles illegally crossing hatched road markings using YOLOv8, ByteTrack, and trajectory-based severity scoring

A Python-based computer vision system that detects vehicles violating red traffic lights using video streams: the system monitors traffic signal status, identifies vehicles crossing during a red phase, and flags those as violations aimed at enhancing intersection safety and automated traffic monitoring.

This project develops a low-cost intelligent traffic violation detection system using AI and geospatial technologies. It detects violations in real time using YOLOv8, ByteTrack, and MediaPipe, applies spatio-temporal logic to reduce false alerts, and logs violations with GNSS data on a map-based dashboard.

This project is to determine the safest and the most dangerous neighborhoods in Chicago and provide suggestions for how to avoid many violations in certain areas.

A high-efficiency traffic violation detection system built with YOLO26n. NanoTraffic identifies motorcycles and bicycles in real-time, specifically targeting pedestrian road incursions, crosswalk violations, and stop-line infractions. Designed as a learning project to master edge-optimized computer vision and the Ultralytics ecosystem.

2026年4月施行「自転車青切符」対応 — ながらスマホ・信号無視・傘差し運転をAIで自動検知 | YOLO + pose estimation + VLM verification

AI Traffic Violation System - AI & IoT Integration project in AI / ML / GenAI for CSEProjects360 final year project catalog.

The aim of the project is to apply different global, local and performance interpretability methods as well as model fairness evaluations to a dataset with protected attributes. The dataset regards traffic violations in Montgomery, Maryland, USA. This is a fork of a group project of my Data Science for Business Master's Degree at HEC Paris.

نظام ذكي محلي لإدارة وإصدار المخالفات المرورية باستخدام FastAPI و React و PostgreSQL و RabbitMQ و MinIO و Celery مع دمج فعلي لنموذج YOLO.