Object Detection Algorithms Based on Convolutional Neural Networks

Abstract

Object detection is a fundamental and challenging task in computer vision, and it has attracted much attention from researchers worldwide. In recent years, deep learning technology has made remarkable progress and enabled new possibilities for object detection. Convolutional neural networks (CNNs), which are powerful tools for feature extraction and representation learning, have become the dominant approach for object detection, surpassing the traditional methods. This article reviews the development history of CNNs and their applications to object detection. It also introduces and compares two main branches of CNN-based object detection algorithms: region-based methods, which use a two-stage pipeline to first generate candidate regions and then classify them, and regression-based methods, which directly predict the bounding boxes and labels of objects in a single stage. Finally, it summarizes the current state-of-the-art and discusses the future directions of object detection research.