# YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/2410.22898 · HTML: https://mlsanity.com/paper/2410.22898/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images](https://mlsanity.com/paper/2408.06784/) (2024)
- [Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection](https://mlsanity.com/paper/2409.12669/) (2024)

## Built on by (most cited first, 9 of 9)

- [YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions](https://mlsanity.com/paper/2411.00201/) (2024)
- [YOLOv11 Optimization for Efficient Resource Utilization](https://mlsanity.com/paper/2412.14790/) (2024)
- [A Simple Detector with Frame Dynamics is a Strong Tracker](https://mlsanity.com/paper/2505.04917/) (2025)
- [Enhanced Vehicle Speed Detection Considering Lane Recognition Using Drone Videos in California](https://mlsanity.com/paper/2506.11239/) (2025)
- [Intelligent Traffic Monitoring with YOLOv11: A Case Study in Real-Time Vehicle Detection](https://mlsanity.com/paper/2604.04080/) (2026)
- [Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking](https://mlsanity.com/paper/2411.17251/) (2024)
- [Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis](https://mlsanity.com/paper/2412.19467/) (2024)
- [Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis](https://mlsanity.com/paper/2511.12018/) (2025)
- [Infectious Bovine Pinkeye Detection Using Computer Vision and Imbalance-Aware Learning](https://mlsanity.com/paper/2609.23714/) (2026)

## Abstract (arXiv)

Accurate vehicle detection is essential for the development of intelligent transportation systems, autonomous driving, and traffic monitoring. This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models, focusing exclusively on vehicle detection tasks. Building upon the success of its predecessors, YOLO11 introduces architectural improvements designed to enhance detection speed, accuracy, and robustness in complex environments. Using a comprehensive dataset comprising multiple vehicle types-cars, trucks, buses, motorcycles, and bicycles we evaluate YOLO11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLO11 surpasses previous versions (YOLOv8 and YOLOv10) in detecting smaller and more occluded vehicles while maintaining a competitive inference time, making it well-suited for real-time applications. Comparative analysis shows significant improvements in the detection of complex vehicle geometries, further contributing to the development of efficient and scalable vehicle detection systems. This research highlights YOLO11's potential to enhance autonomous vehicle performance and traffic monitoring systems, offering insights for future developments in the field.