Deep Learning object detection models: evolution and evaluation

摘要

Computer vision is a subfield of artificial intelligence that relies on training computers to obtain a high level of understanding of vision data. A computer vision system aims at identifying objects through the acquisition of their features such as textures, shapes, sizes, colors, spatial arrangement, to gain an exhaustive description of a video or an image. There are a lot of subfields of computer vision one of them is Object Detection. Detecting objects is a task to identify objects in a specific area. Over the last decades, object detection has gained attention due to its wide range of applications such as human motion analysis, robot navigation, event detection, anomaly detection, video surveillance, traffic analysis, and security. In this paper, we are going to introduce the different Object Detection methodologies and especially relied on Deep Learning based on two categories one stage detectors and two stage detectors. The main goal of this study is to detect, analyze and compare several detection methods and identify the best method based on different several performance metrics ranging from 2014 to 2021. The purpose of this paper to compare some of Object detection methodologies using Weighted Scoring Model (WSM). This covers, studying those algorithms, selecting relevant algorithms. The result of this comparison will show the best Object Detection methods applied on COCO dataset.