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Computer vision is a field of artificial intelligence that trains computers to gain high-level understanding from images or videos. Among the most well-known subfields in Computer Vision are object detection, object tracking, motion estimation, etc. So, object detection is a computer vision technique for detecting instances of objects in images or videos. It can be performed in different ways, either by using delimiting frames or by using object segmentation. It is useful and widely used in the following tasks: image annotation, activity recognition, face recognition, and object tracking. There are two types of approaches to detecting objects in the image: two-stage detector-based approaches and one-stage detector-based approaches with their advantages and disadvantages. Object detection algorithms and techniques generally fall under machine learning approaches and deep learning approaches. Deep learning approaches are based on convolutional neural networks that allow us to perform many tasks, such as clustering, classification, or regression. This paper, which is a survey, aims at reviewing the different approaches and models for deep learning to detect objects in the image and then the advantages and disadvantages of each approach as well as the different fields of application. Also, many data sets that are proposed to evaluate the different methods to detect objects in the image will be presented in this article.
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