Drilling into the SSD Model for Object Detection

Object detection brings up several challenges in pattern recognition and computer vision, such as identifying and detecting various objects, and finding the location of each object in overlapping images. In object detection, the "object" is identified by the image given as input and "location" of that object is traced. Currently, there exists several algorithms that analyze the input image and provide output in terms of the detected objects, where each of them is associated with the class label as well as location (having all the coordinates of the bounding box) [1]. Among all of the object detection approaches, SSD (Single Shot Detector) is considered to be robust and fast since it uses multiple convolution for the detection of object. This blog will discuss about the SSD, its significant parameters and working of SSD.