# Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensembling

arXiv: https://arxiv.org/abs/2108.12118 · HTML: https://mlsanity.com/paper/2108.12118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Car Type Recognition with Deep Neural Networks](https://mlsanity.com/paper/1602.07125/) (2016)
- [Scale Optimization for Full-Image-CNN Vehicle Detection](https://mlsanity.com/paper/1802.06926/) (2018)

## Built on by (most cited first, 6 of 6)

- [Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh](https://mlsanity.com/paper/2212.09144/) (2022)
- [An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification](https://mlsanity.com/paper/2304.06520/) (2023)
- [RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving](https://mlsanity.com/paper/2401.07322/) (2024)
- [Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh](https://mlsanity.com/paper/2410.08230/) (2024)
- [Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security](https://mlsanity.com/paper/2312.02699/) (2023)
- [Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities](https://mlsanity.com/paper/2503.02967/) (2025)

## Abstract (arXiv)

Vehicular object detection is the heart of any intelligent traffic system. It is essential for urban traffic management. R-CNN, Fast R-CNN, Faster R-CNN and YOLO were some of the earlier state-of-the-art models. Region based CNN methods have the problem of higher inference time which makes it unrealistic to use the model in real-time. YOLO on the other hand struggles to detect small objects that appear in groups. In this paper, we propose a method that can locate and classify vehicular objects from a given densely crowded image using YOLOv5. The shortcoming of YOLO was solved my ensembling 4 different models. Our proposed model performs well on images taken from both top view and side view of the street in both day and night. The performance of our proposed model was measured on Dhaka AI dataset which contains densely crowded vehicular images. Our experiment shows that our model achieved mAP@0.5 of 0.458 with inference time of 0.75 sec which outperforms other state-of-the-art models on performance. Hence, the model can be implemented in the street for real-time traffic detection which can be used for traffic control and data collection.