Intelligent Traffic Monitoring with YOLOv11: A Case Study in Real-Time Vehicle Detection

TL;DR

This paper introduces a real-time, offline traffic monitoring system using YOLOv11 and ByteTrack, achieving high accuracy in vehicle detection and counting across diverse scenes without cloud reliance.

Contribution

The paper presents a novel offline traffic monitoring system combining YOLOv11 with ByteTrack, implemented in PyTorch and OpenCV, for efficient vehicle detection and counting in real-time.

Findings

Counting accuracy ranges from 66.67% to 95.83%.

High class-wise detection precision for cars and trucks.

Robust performance in typical weather conditions.

Abstract

Recent advancements in computer vision, driven by artificial intelligence, have significantly enhanced monitoring systems. One notable application is traffic monitoring, which leverages computer vision alongside deep learning-based object detection and counting. We present an offline, real-time traffic monitoring system that couples a pre-trained YOLOv11 detector with BoT-SORT/ByteTrack for multi-object tracking, implemented in PyTorch/OpenCV and wrapped in a Qt-based desktop UI. The CNN pipeline enables efficient vehicle detection and counting from video streams without cloud dependencies. Across diverse scenes, the system achieves (66.67-95.83%) counting accuracy. Class-wise detection yields high precision (cars: 0.97-1.00; trucks: 1.00) with strong recall (cars: 0.82-1.00; trucks: 0.70-1.00), resulting in F1 scores of (0.90-1.00 for cars and 0.82-1.00 for trucks). While adverse weather conditions may negatively impact this performance, results remain robust in typical conditions. By integrating lightweight models with an accessible, cloud-independent interface, this paper contributes to the modernization and development of future smart cities by showing the capacity of AI-driven traffic monitoring systems.