Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations

1. Human Detection in Drone Images Using
YOLO for Search-and-Rescue Operations
Sergio Caputo, Giovanna Castellano, Francesco Greco,
Corrado Mencar, Niccolò Petti, Gennaro Vessio
gennaro.vessio@uniba.it

2. Context
Drones can provide a cost-efficient aid to
search-and-rescue operations:
● swarms of aerial vehicles can be rapidly
spread across a disaster area providing
mobile ad-hoc networks
● they can rapidly overfly and traverse
difficult to reach regions, such as
mountains, islands, etc.
● they can deliver rescue apparatus, such as
medications, much faster than rescue
teams
2

3. Motivations
However, in such a scenario, a manual search performed by a flight operator
(based on the aerial video captured by the drone) can prove extremely difficult:
● it requires a long concentration to perform the flight operation and the
searching task at the same time
● the operator could work in poor conditions, because of the small size of the
monitor he is equipped with, as well as the brightness of the screen outdoor
The use of autonomous drones can reduce manual human intervention, thereby
increasing detection rate, while reducing rescue time
3

4. Goal
This opportunity motivates research efforts
towards the development of real-time intelligent
tools to be mounted directly on-board drones
Nowadays, drones embed quite powerful GPUs,
so even a simple UAV can be transformed into
an advanced computer vision flying machine
4

5. Detection method
We considered the lighter versions of YOLOv5:
● YOLOv5s (small-size)
● YOLOv5m (medium-size)
YOLOv5 is different from all other previous
versions; in particular, it introduced mosaic data
augmentation and the ability to autonomously
learn bounding box anchors
5

6. Datasets
● HERIDAL
○ 1700 images of wildlife at high resolution
● SARD
○ 1981 images of wildlife at high resolution
○ pose estimation labels
6

7. Setting
● Software:
○ Roboflow library
● Hardware:
○ Google Colab NVIDIA Tesla K80
● Hyper-parameter setting:
○ Pretraining: COCO
○ Mini-batch size: 32
○ Learning rate: 0.01
○ Early stopping on a validation set
○ Input: 800×800
○ IoU: 50%
● Performance metrics:
○ Precision
○ Recall
○ AP
7

8. Results
8

9. Results
9

10. Conclusion
Promising human detection performance using the latest YOLOv5 detection
algorithm have been obtained
Future work: improve the results obtained on the multi-class classification based
on human pose by properly augmenting the under-represented classes
Thanks for the attention!
10