# CNN-based Human Detection for UAVs in Search and Rescue

arXiv: https://arxiv.org/abs/2110.01930 · HTML: https://mlsanity.com/paper/2110.01930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 5 Oct 2021.

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

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Face Detection with the Faster R-CNN](https://mlsanity.com/paper/1606.03473/) (2016)
- [Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)](https://mlsanity.com/paper/2105.01580/) (2021)

## Built on by (most cited first, 1 of 1)

- [Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification](https://mlsanity.com/paper/2204.00943/) (2022)

## Abstract (arXiv)

The use of Unmanned Aerial Vehicles (UAVs) as a substitute for ordinary vehicles in applications of search and rescue is being studied all over the world due to its flexible mobility and less obstruction, including two main tasks: search and rescue. This paper proposes an approach for the first task of searching and detecting victims using a type of convolutional neural network technique, the Single Shot Detector (SSD) model, with the Quadcopter hardware platform, a type of UAVs. The model used in the research is a pre-trained model and is applied to test on a Raspberry Pi model B, which is attached on a Quadcopter, while a single camera is equipped at the bottom of the Quadcopter to look from above for search and detection. The Quadcopter in this research is a DIY hardware model that uses accelerometer and gyroscope sensors and ultrasonic sensor as the essential components for balancing control, however, these sensors are susceptible to noise caused by the driving forces on the model, such as the vibration of the motors, therefore, the issues about the PID controller, noise processing for the sensors are also mentioned in the paper. Experimental results proved that the Quadcopter is able to stably flight and the SSD model works well on the Raspberry Pi model B with a processing speed of 3 fps and produces the best detection results at the distance of 1 to 20 meters to objects.