A Review of the Single-Stage vs. Two-Stage Detectors Algorithm:Comprehensive Insights into Object Detection

Abstract

Object Detection is the most common and tough issue in the field of computer vision. Deep learning has advanced enormously in the last 10 years; it has encouraged researchers to use very basic deep models to explore the effective improvement of object detection and correlated tasks. These tasks include classification, localization, and segmentation. One can broadly categorize object detectors into two categories: two-stage and single-stage detectors. Two-stage detectors take most of their support from designs that first propose regions. On the other hand, single-stage detectors focus all their attention on the use of simple settings to propose all regions at once for object detection. Single-stage detectors, however, are faster in terms of computation time. For accuracy, the YOLO algorithm and its variants sometimes outperform two-stage detectors, which is largely influenced by the Mean Average Precision (mAP) metric. YOLO is popular because it is very fast in processing rather than accurate in its detection. This paper pushes forward full-fledged one-stage object recognizers many incarnations of YOLO two-stage recognizers various flavors of YOLO and some alternative approaches in the realm of object detection.