Object detection has received a significant attention from researchers in recent years because of its close relationship with video analysis and image understanding. Traditional object detection approaches are built on the basis of classifiers and shallow trainable architectures. Their performance can easily plateau by developing sophisticated ensembles that incorporate various low-level visual features with high-level context from object detectors and scene classifiers. More powerful tools that can learn semantic, high-level, and deeper features are being offered with the rapid growth of deep learning to address the difficulties that afflict traditional systems. These models react differently in terms of network architecture, training approach, and optimization function. We present a review of deep learning-based object detection frameworks in this paper. Object recognition tasks, on the other hand, are more difficult to assess, consume more energy, and necessitate more computing power than image categorization. To address these issues, a novel technique for real-time object detection application is being developed in order to improve the detection process' accuracy and energy efficiency.

Keywords : CNN, F-RCNN, Dataset, Images, Videos.