Review of Research Progress in Object Detection Driven by Deep Learning

In recent years, deep learning, driven by high-performance GPU computing, has rapidly expanded into security, healthcare, and industry. Object detection models have evolved from traditional methods to convolutional neural networks (CNN), significantly saving resources. This review outlines the development of object detection and recent advances in deep learning by referencing extensive literature and following a two-stage framework. It compares model performance across different datasets, summarizes the strengths and weaknesses of various methods, and highlights key datasets. The review also discusses the practical applications of object detection algorithms, particularly in autonomous driving, medical imaging, and remote sensing. Finally, it explores the opportunities and challenges for future research in deep learning-driven object detection. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.