A Comprehensive Review On Two-Stage Object Detection Algorithms

Two-stage object detection algorithms have gained significant attention in computer vision due to their robustness and accuracy in detecting objects in images. These algorithms consist of two main stages: region proposal generation and object classification/localization. The region proposal stage efficiently generates a diverse set of candidate object regions using various techniques, narrowing down the search space. In the second stage, deep convolutional neural networks extract discriminative features from the proposed regions, enabling precise object classification and localization. These algorithms have demonstrated impressive performance in complex scenes with occlusions, scale variations, and multiple objects. However, they have limitations such as slower inference speeds, increased computational complexity, and the need for additional training steps. To address these limitations, more efficient algorithms like single-stage detectors have emerged. Two-stage object detection algorithms offer a powerful framework for accurate and robust object detection, and ongoing research aims to refine and improve these algorithms, expanding the capabilities of computer vision applications.