Abstract
Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200 images. The accuracy of the system’s prediction for the target object was 97.5%.
Highlights
IntroductionAnimal detection methods by Computer Vision (CV) are helpful to solve various problems such as the wildlife road accidents as well as threatened and endangered species, in addition to other purposes for animal detection and classification [1 2]
Animal detection methods by Computer Vision (CV) are helpful to solve various problems such as the wildlife road accidents as well as threatened and endangered species, in addition to other purposes for animal detection and classification [1 2].Computer Vision (CV) has become widespread in our society, with applications in image understanding, medicine, mapping, self-driving cars, and drones [3, 4]
We develop an automated and fast algorithm that can assist in future robots in detecting vertebrate animals in still images and classify the two main groups of vertebrates, i.e. Mammals and Reptiles
Summary
Animal detection methods by Computer Vision (CV) are helpful to solve various problems such as the wildlife road accidents as well as threatened and endangered species, in addition to other purposes for animal detection and classification [1 2]. Schneider et al (2018) proposed a network to train and compare two classifiers of deep learning object detection, namely Faster YOLO v2.0 and R-CNN, to recognize, classify, and localize species of animals within images of camera-trap using datasets. The block diagram of the proposed training and testing algorithms for detection and classification of the two categories (mammals and reptiles) is shown in Figures-(3 and 4). The results of our proposed network for the detection and classification of the two classes of vertebrates, when using an images size of 50x50 and several epochs that is equal to 100, are as follows: When the test images were for Reptiles, the system predicted all these images successfully.
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