Scorpion detection and classification systems based on computer vision and deep learning for health security purposes
This provides a health security tool for emergency services and biological research in a specific region, with potential scalability to other scorpion species.
The paper developed two real-time systems using YOLO and MobileNet for detecting and classifying scorpions in La Plata, Argentina, achieving high accuracy (88-91%) and recall (90-97%) to distinguish dangerous from non-dangerous species.
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.