Performance Evaluation of Object Detection Algorithm Using Ant Colony Optimization Based Image Segmentation

Object detection is a very important application of image processing. It is of vital importance for object dynamic surveillance and other applications. So far, object detection has been widely researched. It shows an efficient coarse object locating method based on a saliency mechanism. The method could avoid an exhaustive search across the image and generate a small number of bounding boxes. After that, the trained DBN is used for feature extraction and classification on sub-images. This paper represents that the a variety of strategies based on object detection and efficiency of object detection framework using a saliency prior and DBNs for remote sensing images. This research works proposed an efficient object detection using the ant colony optimization and deep belief networks. The motivation behind the proposed approach is easy and efficient.