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In this paper, two novel automatic and real-time systems for the detectionand 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 thescorpions. High accuracy values of 88% and 91%, and high recall values of 90%and 97%, have been achieved for both models, respectively, which guaranteesthat they can successfully detect scorpions. In addition, the MobileNet methodhas been shown to have excellent performance to detect scorpions within anuncontrolled environment and to perform multiple detections. The MobileNetmodel was also used for image classification in order to successfullydistinguish between dangerous scorpion (Tityus) and non-dangerous scorpion(Bothriurus) with the purpose of providing a health security tool. Applicationsfor smartphones were developed, with the advantage of the portability of thesystems, which can be used as a help tool for emergency services, or forbiological research purposes. The developed systems can be easily scalable toother genera and species of scorpions to extend the region where theseapplications can be used.

Scorpion detection and classification systems based on computer vision and deep learning for health security purposes