Novel automatic scorpion-detection and -recognition system based on machine-learning techniques

All species of scorpions can inject venom, some of them even with the possibility of killing a human. Therefore, early detection and identification are essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using computer vision and machine learning (ML) approaches. Two complementary image-processing techniques were used for the proposed detection method to accurately and reliably detect the presence of scorpions. The first is based on the fluorescent characteristics of scorpions when exposed to ultraviolet light, and the second on the shape features of the scorpions. Also, three models based on ML algorithms for the image recognition and classification of scorpions are compared. In particular, the three species of scorpions found in La Plata city (Argentina): Bothriurus bonariensis (of no sanitary importance), Tityus trivittatus, and Tityus confluence (both of sanitary importance) have been researched using a local binary-pattern histogram algorithm and deep neural networks with transfer learning (DNNs with TL) and data augmentation (DNNs with TL and DA) approaches. A confusion matrix and a receiver operating characteristic curve were used to evaluate the quality of these models. The results obtained show that the model of DNN with TL and DA is the most efficient at simultaneously differentiating between Tityus and Bothriurus (for health security) and between T. trivittatus and T. confluence (for biological research purposes).