Landmark Detection Using Squeeze and Excitation Residual Neural Networks

Abstract

Landmark Recognition is the technology that can anticipate landmark names straightforwardly from picture pixels, to help individuals better comprehend and sort out their photograph accumulations and for law enforcement officials to gauge the location of images submitted as evidence. Image classifications techniques have shown remarkable improvements over the last few years. To further improve computer vision technologies and methodologies, researchers are now concentrating on highly specific types of classification. Instead of classifying cats, cars or buildings, researchers are trying to classify among different types of landmarks - both natural and man-made. In the present age, a tremendous roadblock in landmark recognition research is the lack of large, well labelled datasets. To rectify this, Google has come up with the Google Landmark Recognition Dataset. The dataset contains 1.2 million images of 15000 categories of landmarks. For the project, a subset of Google Landmark Recognition dataset has been used. Various latest classification algorithms, like AlexNet, ResNet, SE-ResNet, VGG-16 and Inception v3 have been implemented to classify the images. Among them, the SE-ResNet architecture achieves the lowest loss value of 0.1822 and accuracy of 94.08% on the training set.