Landmark Detection System

Abstract

A tool based on machine learning, known as the Landmark Detection System, is utilized to locate and recognize significant landmarks or critical regions within images. By employing advanced computer vision techniques, particularly convolutional neural networks (CNNs), the system can accurately identify and detect landmarks across diverse environments. The research follows a systematic methodology that begins with data preparation, which involves the collection and annotation of a large dataset of landmark images. Subsequently, these images are standardized to ensure uniformity in size and format. During the model development phase, a CNN is trained using pre-trained networks such as ResNet or VGG to capture the visual characteristics specific to landmarks. Performance is further enhanced through the application of transfer learning techniques. Once training is complete, the model is assessed on new images to evaluate its accuracy in landmark identification.