Object Detection Algorithms Based on Deep Learning: A Review

Abstract

With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional object detection algorithms and their existing problems, then elaborates on the main processes, innovations, advantages, disadvantages, and experimental results on datasets of deep learning-based object detection algorithms. It focuses on the development of Two-Stage and One-Stage object detection algorithms, and provides an outlook on the future development of object detection algorithms, discussing challenges such as the coordination of detection speed and accuracy, difficulties in detecting small objects, real-time detection tasks, and multi-modal fusion applications, and proposes possible future directions.