  In this paper, two novel automatic and real-time systems for the detection
and classification of two genera of scorpions found in La Plata city
(Argentina) were developed using computer vision and deep learning techniques.
The object detection technique was implemented with two different methods, YOLO
(You Only Look Once) and MobileNet, based on the shape features of the
scorpions. High accuracy values of 88% and 91%, and high recall values of 90%
and 97%, have been achieved for both models, respectively, which guarantees
that they can successfully detect scorpions. In addition, the MobileNet method
has been shown to have excellent performance to detect scorpions within an
uncontrolled environment and to perform multiple detections. The MobileNet
model was also used for image classification in order to successfully
distinguish between dangerous scorpion (Tityus) and non-dangerous scorpion
(Bothriurus) with the purpose of providing a health security tool. Applications
for smartphones were developed, with the advantage of the portability of the
systems, which can be used as a help tool for emergency services, or for
biological research purposes. The developed systems can be easily scalable to
other genera and species of scorpions to extend the region where these
applications can be used.
