Automatic Unwanted Foreground Object Detection in Images Using Deep Learning

Source abstract

When taking pictures, there is often an unwanted foreground in the image, which may degrade the visual quality of the images. Separating unwanted foreground and background layers is an essential problem for enhancing image quality. However, image unwanted foreground detection and removal is a challenging process because of the ill-posed nature of the problem. Most previous works tackle this problem using multiple images or hand-crafted features. These methods are still quite limited in terms of generality and photo-realistic results. Detecting unwanted foregrounds automatically from the photos is highly desirable. This research presents an automated unwanted foreground detection technique based on deep learning. The model automatically detects the four types of unwanted foreground, including a fence, rain, fog, and shadow, from an image without degradation in image quality. The dataset used in this work consists of images taken from online repositories and self-captured. For shadow, the ISTD Dataset is used, a de-fencing dataset is used for fencing, and the Weather Image Recognition dataset images are used for fog and rain. Moreover, each type of unwanted foreground image is captured from the real world. To perform the unwanted foreground type detection, VGG-16 deep learning model is used. The experimental results indicate that the proposed system performs unwanted foreground detection with an accuracy of 97.13%. This research work can be used as first step towards the removal of unwanted foreground.