Detection of Deer in Images by Computer Vision Methods

An approach to applying machine learning methods for automatic detection of deer individuals in images has been studied. Neural network technology has been used to accurately count the number of deer from photographs. Deep learning methods for convolutional neural networks (ResNet 50, DenseNet, CenterNet, Inception V3, and Xception) were used in conjunction with the transfer learning technique. Based on the faster R-CNN Resnet50 network, a neural network was trained to identify deer individuals from graphic images with an accuracy of 0.91 on a sample using the F1-score metric with a threshold value of 0.6.