# What is YOLOv5: A deep look into the internal features of the popular object detector

arXiv: https://arxiv.org/abs/2407.20892 · HTML: https://mlsanity.com/paper/2407.20892/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Cross-Iteration Batch Normalization](https://mlsanity.com/paper/2002.05712/) (2020)

## Built on by (most cited first, 30 of 37)

- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector](https://mlsanity.com/paper/2408.15857/) (2024)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector](https://mlsanity.com/paper/2409.07813/) (2024)
- [VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery](https://mlsanity.com/paper/2510.04479/) (2025)
- [Image-Goal Navigation Using Refined Feature Guidance and Scene Graph Enhancement](https://mlsanity.com/paper/2503.10986/) (2025)
- [Less Redundancy: Boosting Practicality of Vision Language Model in Walking Assistants](https://mlsanity.com/paper/2508.16070/) (2025)
- [Real-Time Oriented Object Detection Transformer in Remote Sensing Images](https://mlsanity.com/paper/2603.15497/) (2026)
- [Rethinking Video Human-Object Interaction: Set Prediction over Time for Unified Detection and Anticipation](https://mlsanity.com/paper/2604.10397/) (2026)
- [Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robots](https://mlsanity.com/paper/2502.03829/) (2025)
- [High-Frequency Semantics and Geometric Priors for End-to-End Detection Transformers in Challenging UAV Imagery](https://mlsanity.com/paper/2507.00825/) (2025)
- [An Analysis of Kalman Filter based Object Tracking Methods for Fast-Moving Tiny Objects](https://mlsanity.com/paper/2509.18451/) (2025)
- [Heatmap Pooling Network for Action Recognition from RGB Videos](https://mlsanity.com/paper/2512.03837/) (2025)
- [Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking](https://mlsanity.com/paper/2605.25538/) (2026)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)
- [Bridging the Catalog-to-Real Gap: Scalable Product Recognition via Multi-Stage Contrastive Learning](https://mlsanity.com/paper/2607.09888/) (2026)
- [Calibration-Free 3D Multi-Camera People Tracking for Indoor Environment](https://mlsanity.com/paper/2607.22731/) (2026)
- [A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions](https://mlsanity.com/paper/2504.11995/) (2025)
- [DMS-Net:Dual-Modal Multi-Scale Siamese Network for Binocular Fundus Image Classification](https://mlsanity.com/paper/2504.18046/) (2025)
- [Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)](https://mlsanity.com/paper/2505.10770/) (2025)
- [YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries](https://mlsanity.com/paper/2507.05376/) (2025)
- [Detecci'on y Cuantificaci'on de Erosi'on Fluvial con Visi'on Artificial](https://mlsanity.com/paper/2507.11301/) (2025)
- [Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images](https://mlsanity.com/paper/2509.02957/) (2025)
- [Can You Trust What You See? Alpha Channel No-Box Attacks on Video Object Detection](https://mlsanity.com/paper/2510.19574/) (2025)
- [Physics-Guided Fusion for Robust 3D Tracking of Fast Moving Small Objects](https://mlsanity.com/paper/2510.20126/) (2025)
- [Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning Framework with Vision-Language Models](https://mlsanity.com/paper/2511.14120/) (2025)
- [MT-Depth: Multi-task Instance feature analysis for the Depth Completion](https://mlsanity.com/paper/2512.04734/) (2025)
- [AurigaNet: A Real-Time Multi-Task Network for Enhanced Urban Driving Perception](https://mlsanity.com/paper/2602.10660/) (2026)
- [Alignment-Aware and Reliability-Gated Multimodal Fusion for Unmanned Aerial Vehicle Detection Across Heterogeneous Thermal-Visual Sensors](https://mlsanity.com/paper/2603.08208/) (2026)
- [IUP-Pose: Decoupled Iterative Uncertainty Propagation for Real-time Relative Pose Regression via Implicit Dense Alignment v1](https://mlsanity.com/paper/2603.19625/) (2026)

## Abstract (arXiv)

This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explored in detail. The paper reviews the model's performance across various metrics and hardware platforms. Additionally, the study discusses the transition from Darknet to PyTorch and its impact on model development. Overall, this research provides insights into YOLOv5's capabilities and its position within the broader landscape of object detection and why it is a popular choice for constrained edge deployment scenarios.