In the field of computer vision, object detection is the fundamental most widely used and challenging problem. Last several decades, great effort has been made by computer scientists or researchers to handle the object detection problem. Object detection is basically, used for detecting the object from image/video. At the beginning of the 21st century, a lot of work has been done in this field such as HOG, SIFT, SURF etc. are performing well but can’t be efficiently used for Real-time detection with speed and accuracy. Furthermore, in the deep learning era Convolution Neural Network made a rapid change and leads to a new pathway and a lot of excellent work has been done till dated such as region-based convolution network YOLO, SSD, retina NET etc. In this survey paper, lots of research papers were reviewed based on popular traditional object detection methods and current trending deep learning-based methods and displayed challenges, limitations, methodologies used to detect the object and also directions for future research.