Analysis of object recognition trends based on deep learning

Abstract

With the increasing development and maturity of deep learning, computers have also made world-renowned achievements in the domain of vision, especially in the basic and core branch of object detection, giving birth to many classical algorithms, which are widely used in many fields such as autonomous driving, intelligent medical care, intelligent security, and search entertainment. Before the emergence of deep learning algorithms, traditional algorithms for object detection were usually divided into three stages: region selection, feature extraction, and feature classification. However, with the advent of deep learning algorithms, object detection has taken to another peak, with Single Shot MultiBox Detector (SSD) enabling first-order detection of multi-feature maps and Region-based Convolutional Neural Networks (R-CNN) improving the performance of object detection while enabling instance segmentation. For object detection, this paper investigates the traditional algorithms, R-CNN, SSD, You Only Look Once (YOLO), and diffusion model, which is influential detection algorithms, and compares their differences as well as advantages in object detection to provide a reference for related research.