A study on generic object detection with emphasis on future research directions

Abstract

Object detection is an essential task in computer vision and image processing. It has many applications in various domains like medical diagnosis, civil military, video surveillance, security, etc. In some vision-related approaches, object detection used as an integral part, such as semantic segmentation, instance segmentation, pose estimation, suspicious activity detection, etc. The first stage in the pipeline is to detect an object. The survey begins with significant highlights of deep learning for object detection. It provides a comprehensive study on object representation; Convolution Neural Network (CNN) and different Deep Convolution Neural Network architecture. It presents a concise review of renowned datasets and definitive measurement metrics, forming the primitive baseline to evaluate the detection framework. Then studies in detail on detection framework one-stage and two-stage detectors and evaluates each framework with standard datasets listing its vital significance. The study also explores different issues of object detection like multi-scale, intra-class variations, generalization & security. Moreover, lists the primitive steps for creating object detector for different conditions from the reviewed survey. Finally, it proposes promising research directions.