Skip to content
Research Article Open access CC BY 4.0

Performance Benchmarking of YOLOv11 Variants for Real-Time Delivery Vehicle Detection: A Study on Accuracy, Speed, and Computational Trade-offs

Rabinandan Kishor

Asian Journal of Research in Computer Science · pp. 108–122 · Published 12 Dec 2024

10.9734/ajrcos/2024/v17i12532

Abstract

The YOLOv series represents state-of-the-art technology for single-stage object detection, excelling in speed and accuracy. In many scenarios, it outperforms traditional two-stage detection frameworks, making it ideal for real-time applications. This study evaluates YOLOv11 model variants (n, s, m, i, x) on a custom dataset of 2,285 labelled images representing four delivery vehicle classes: FedEx, Other-Vehicles, UPS, and USPS-Truck. The dataset is meticulously curated to capture diverse delivery vehicle scenarios and split into training, validation, and test sets. Each variant was fine-tuned using uniform settings: 20 epochs, an input resolution of 640×640 pixels, and a batch size of 16. Performance was assessed using metrics such as mean Average Precision (mAP, a standard metric measuring detection accuracy) across Intersection over Union (IoU) thresholds from 50% to 95% (a range defining the overlap between predicted and ground-truth bounding boxes), precision, recall, and inference speed on GPU and CPU. The results highlight trade-offs between model complexity and performance: smaller variants like YOLOv11-n achieved faster inference speeds (170.74 FPS on GPU and 5.86 ms on GPU), while larger models like YOLOv11-x excelled in detection accuracy and recall but at the cost of slower speeds (240.03 FPS on GPU and 4.17 ms on GPU). YOLOv11-s, for example, offered a balance with the highest FPS (1120.46 GPU FPS) but with moderate accuracy and recall. These findings demonstrate the adaptability of YOLOv11 variants to varying application requirements, from high-speed real-time systems to scenarios prioritizing detection accuracy. This research advances object detection by providing a detailed performance benchmark for YOLOv11 variants. It offers practical insights for deploying YOLOv11 in diverse fields, including logistics, delivery tracking, and other domains requiring efficient and accurate object detection.

YOLO YoloV11 object detection models deep learning computer vision neural Networks dataset evaluation model performance metrics image processing real-time applications

References (1)

  1. 1 Understanding of Object Detection Based on CNN Family and YOLO [DOI]

Cited by 44

YOLOv11-ASM: Enhancing Real-Time Object Detection for Autonomous Driving

Yi Wu, Yuhang Jin · Highlights in Science, Engineering and Technology · 2025

Real-time safety detection on construction sites using a vision-language and NLP-based model

S. Sivanraj, D.N.L.S Uduwage, M. Tripathi · Advanced Engineering Informatics · 2026

Stable Vision-Based Robot Kinematic Control With Deep Learning-Based Oriented Object Detector

Sitan Li, Chao Liu, Koji Matsuno · IEEE Robotics and Automation Letters · 2026

PushPull-YOLO: A biologically inspired framework for robust object detection under image corruptions

Hasan Ali Akyürek · Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi · 2025

Application of machine learning-based object detection and tracking techniques for understanding bubble formation behavior on falling film evaporator tube surface

Van Cong Le, Van Hau Duong, Tan Loc Huynh · Results in Engineering · 2025

Real-Time Multi-Damage Detection and Risk Prioritisation for Aging Buildings Using YOLOv11 and a Damage Criticality Index

Jongnam Ho, Yonghan Ahn, Hyunkyu Shin · Sustainability · 2025

Accuracy–Latency Trade-offs and Computational Complexity of Ultralytics YOLOv11 for Real-Time Video Object Detection

Kholidiyah Masykuroh, Hendrawan Hendrawan, Eueung Mulyana · 2025 19th International Conference on Telecommunication Systems, Services, and Applications (TSSA) · 2025

Efficacy of deep learning models and dental professionals in identifying dental implants

Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli · Imaging Science in Dentistry · 2025

Lens-Viewpoint Extraction Method for Personalized Progressive Addition Lens Fitting

Yating Yang, Xiaoqin Chen, Zhichao Deng · Translational Vision Science & Technology · 2025

Toward Real-Time Posture Classification: Reality Check

Hongbo Zhang, Denis Gračanin, Wenjing Zhou · Electronics · 2025

Showing 14 of 44 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

14

Page views

0

PDF downloads

2

Outbound clicks

44

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.