Snake species classification using deep learning techniques

Abstract

Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. The classification of snake species has a significant role in determining the appropriate treatment without any delay, the delay may cause dangerous complications or lead to the death of the victim. The difficulty of classifying snakes by human lies in the variations of snake pattern based on geographic variation and age, the intraclass variance is high for specific classes and the interclass variance is low among others, and there may be two remarkably similar types in shape, with one being toxic and the other not. The limitation of the experts’ number in the herpetology and their geographical distribution leads us to the importance of using deep learning in the snake species classification. A model to classify snake species accately is proposed in this study. It is divided into two main processes, detecting the salient object by applying Salient Object Detection (SOD) model based on VGG16 architecture is the first process, the presence of snakes in places with a complex background led to the necessity of separating the salient object, then the classification model is applied with use of image augmentations parameters which improved the results. Four CNN models were used in the classification process including VGG16, ResNet50, MobileNetV2, and DenseNet121. Different experiments on 5,10,16,20, 22, and 45 number of classes and different models were conducted, and the model achieved unprecedented results. The results indicated that the VGG16, DenseNet121, and MobileNetV2 have achieved superior results in the same order from highest to lowest accuracy. The best accuracy is achieved using VGG16 architecture with accuracy 97.09% when using 45 number of classes.