Free duplicate photo cleaner recognition

After the removal of the duplicated images and crowdsourcing efforts to label the images, they fed these images into an image classifier to identify damaged structures, injured people and rescue efforts. They used the pre-trained VGG-16 model with fine-tuning to filter the relevant images from the collected images on Twitter. 4 provided a social media image processing pipeline to combine human and machine intelligence to extract information from images as a situational awareness task during a crisis event, such as earthquakes or hurricanes. 3 used a transfer learning approach, more specifically, domain-specific fine-tuning Convolution Neural Network (CNN) to estimate the damage severity in the images on social media platforms after a disaster. There are existing efforts to extract useful information from these crowdsourcing images to better understand natural disasters. Such images or videos taken by individuals or news media often contain important information about the damage, such as damaged buildings, bridges, roads and other infrastructures 1, 2. With the help of current technology, images of this damage can be captured soon after the earthquake and uploaded online so that people in other parts of the world can easily browse through these images and videos to obtain a sense of the consequences of the disaster. Large earthquakes near populated areas leave unforgettable testimony of their intimidating powers: collapsed buildings, offset roads, disrupted lives, etc. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey.

Using transfer learning andвЂ‰~вЂ‰6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days.

Free duplicate photo cleaner recognition