Comprehensive Survey on Deep Learning-Based Advances in One-Stage Object Detection

Abstract

Object detection plays a crucial role in computer vision applications, ranging from autonomous vehicles to surveillance systems. One-stage object detectors have emerged as a powerful solution by simplifying detection pipelines, enabling real-time performance while maintaining competitive accuracy. This article provides a comprehensive survey of one-stage obj ect detection models, including you only look once (YOLO), single shot multibox detector (SSD), and PaddlePaddle YOLO (PP-YOLO) variants. A central question guides this exploration: How can incremental architectural refinements and training strategies improve the trade-off between speed, accuracy, and computational efficiency in object detection models for real-time applications? Through a detailed analysis of key innovations, performance metrics, and practical applications, this survey highlights the evolution of these models and their contributions to bridging the gap between research and deployment. Additionally, the paper identifies current challenges, such as small-object detection and resource efficiency, and proposes future research directions to advance the field further.