A survey of Region-based Object Detection models

Object detection is a method in image processing and machine vision that is used to locate samples of objects in images or videos. Object detection algorithms usually use machine learning methods or deep learning methods. The object recognition methods can be single-stage or two-stage. At the same time, these methods are based on the region, based on the transformer and based on pre-training. Also, these methods are based on supervised pre-training or self-monitoring pre-training. Considering that region-based convolutional neural networks are a family of convolutional neural network models that are used to detect objects, and have the characteristic that in the case of classification deficiencies, in addition to extracting feature, more components are also taught in advance, in this article, the method of detecting objects based on the region, according to the mentioned categories, is discussed. Also, the performance criteria used in the detection methods of mentioned objects are introduced.