Scorpion Detection and Classification Systems Based on Computer Vision as a Prevention Tool

Abstract

In this paper, automatic and real-time systems were developed to detect and classify two different genera of scorpions using computer vision and deep learning techniques, with the purpose of providing a prevention tool. The images of scorpions were obtained from an arachnology laboratory in Argentina. YOLO (you only look once) and MobileNet models were implemented. The data augmentation technique was applied to significantly increase the amount of training data. High accuracy and recall values have been achieved for both models, which guarantees that they can early and successfully detect scorpions. In addition, the MobileNet model has shown to have excellent performance to detect scorpions within an uncontrolled environment, to carry out multiple detections, and to recognize their danger in case of accidents. Finally, a comparison has been made with other different machine learning-based models used to identify scorpions.