Paper Title
A Review on Object Detection with Convolutional Neural Network

Object detection is one of the most common issues in computer vision. Various researchers have contributed to several application fields, including robots, self-driving cars, and video surveillance. The real-time object detection methods that use deep learning methods are reviewed in this work. Its purpose is to make the readers familiar with the pertinent information, literature, and most recent advancements in cutting-edge methods. Using three sets of keywords: Deep learning, object detection, and convolutional neural networks. The framework for object detection has two unique groups of detectors: conventional detectors and deep learning-based detectors. Deep learning object detectors come in two varieties: onestage detectors and two-stage detectors. Two-stage detectors produce sparse area recommendations in the first phase before performing regression and classification, in contrast to one-stage detectors that perform classification and regression using dense anchor boxes without first constructing a collection of sparse regions of interest. Crop harvesting, object detector models for the blind, pedestrian identification on the road, traffic sign recognition and classification, text detection, and remote sensing target detection are all applications of object detection. We suggest creating a one-stage object detection model in our upcoming research to aid in directing blind movements. Keywords - Deep learning, Object Detection, Convolutional Neural Networks.