Architecture review: Two-stage and one-stage object detection

Abstract

• The architecture of two and one-stage object detectors was reviewed. • Developing two-stage detectors such as Fast RCNN has improved the detection accuracy. • The evolution of one-stage detectors like YOLO is suitable for real-time work. Object detection has obtained significant attention as a fundamental and challenging task in computer vision in the past two decades. When highlighting the evolution in object detection architecture, clear structural differences can be distinguished between two-stage and one-stage detectors, each of which is significantly shaped by advances in convolutional neural networks (CNNs). Two-stage detectors, including R-CNN and its later developed models, utilize a sequential methodology that initially produces region proposals, subsequently classifying and further refining them. This methodology, as illustrated by models such as Faster R-CNN and Mask R-CNN, incorporates potent feature extraction strategies, such as Feature Pyramid Networks (FPN), thereby improving performance across diverse object metrics such as mean average precision, where the R-CNN design rich a high mean average precision (mAP) of 53.3 % on the PASCAL VOC dataset, which is over 30 % better than older methods. On the flip side, one-stage detectors, represented by the YOLO series, RetinaNet, and SSD, embrace a more integrated architecture that compresses detection tasks into a singular stage, managing to attain notable speed but at the cost of some localization accuracy. Both paradigms are fundamentally rooted in CNN architectures, signifying per- sistent advancements in harmonizing accuracy, speed, and computational efficiency within contemporary object detection systems. This paper reviews the architecture of prominent two-stage object detectors starting with RCNN and its successors and one-stage detectors including the YOLO family. This paper objectives to provide an understanding of the archi- tecture of two-stage and one-stage object detectors and the evolution in their architecture that leads to improved performance in terms of accuracy and speed.

Topics & Concepts