# Dense Crowds Detection and Counting with a Lightweight Architecture

arXiv: https://arxiv.org/abs/2007.06630 · HTML: https://mlsanity.com/paper/2007.06630/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments](https://mlsanity.com/paper/2203.13792/) (2022)
- [Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation](https://mlsanity.com/paper/2410.12988/) (2024)
- [Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments](https://mlsanity.com/paper/2505.20423/) (2025)
- [ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles](https://mlsanity.com/paper/2503.14719/) (2025)

## Abstract (arXiv)

In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting using fewer computer resources without a significant loss on count accuracy. The architecture was trained using the Bayes loss function to further improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF achieving a competitive Mean Average Error of 154.07 and a superior Mean Square Error of 241.77 while maintaining a competitive number of parameters of 0.067 Million. The obtained results suggest that the Bayes loss can be used with other architectures to further improve them and also the last convolutional layer provides no significant information and even encourage over-fitting at training.