# Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis

arXiv: https://arxiv.org/abs/2408.12550 · HTML: https://mlsanity.com/paper/2408.12550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [YOLOv6 v3.0: A Full-Scale Reloading](https://mlsanity.com/paper/2301.05586/) (2023)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety](https://mlsanity.com/paper/2407.20872/) (2024)
- [Fall Detection for Industrial Setups Using YOLOv8 Variants](https://mlsanity.com/paper/2408.04605/) (2024)

## Built on by (most cited first, 2 of 2)

- [Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks](https://mlsanity.com/paper/2411.18288/) (2024)
- [What is YOLOv6? A Deep Insight into the Object Detection Model](https://mlsanity.com/paper/2412.13006/) (2024)

## Abstract (arXiv)

Vehicle detection is an important task in the management of traffic and automatic vehicles. This study provides a comparative analysis of five YOLOv5 variants, YOLOv5n6s, YOLOv5s6s, YOLOv5m6s, YOLOv5l6s, and YOLOv5x6s, for vehicle detection in various environments. The research focuses on evaluating the effectiveness of these models in detecting different types of vehicles, such as Car, Bus, Truck, Bicycle, and Motorcycle, under varying conditions including lighting, occlusion, and weather. Performance metrics such as precision, recall, F1-score, and mean Average Precision are utilized to assess the accuracy and reliability of each model. YOLOv5n6s demonstrated a strong balance between precision and recall, particularly in detecting Cars. YOLOv5s6s and YOLOv5m6s showed improvements in recall, enhancing their ability to detect all relevant objects. YOLOv5l6s, with its larger capacity, provided robust performance, especially in detecting Cars, but not good with identifying Motorcycles and Bicycles. YOLOv5x6s was effective in recognizing Buses and Cars but faced challenges with Motorcycle class.