Performance analysis of deep learning object detection based image segmentation methods

Image segmentation which divides the input image into multiple regions or segments is one of the most difficult problems to be solved in the area of computer vision. Without image segmentation, understanding the images by computer is not an easy process. There are two types of image segmentation task: i) semantic segmentation, in which multiple objects from the same class are considered as the same, ii) instance segmentation where multiple objects from the same class are taken as different, therefore, each object (instance) is to be classified separately. Image segmentation is used in numerous applications such as satellite image processing, medical image processing, texture recognition, face recognition systems, automated plate recognition systems, etc. In this thesis, our aim is to apply deep learning-based object detection to perform semantic segmentation and evaluate its performance. Therefore, first, we applied YOLO to find the bounding boxes of each object in the images, then we used GrabCut and DeepGrabCut methods to classify foreground and background pixels to make semantic segmentation. DeepGrabCut is a deep learning-based version of GrabCut, and we compared their performances on two image datasets having more than 35K images. The experimental analysis has shown that object detection with object selection can be used to make semantic segmentation with acceptable error rates when optimal parameters setting was done for the methods applied. Keywords: Deep learning, prediction, image segmentation, object detection, computer vision