Paper Contents
Abstract
Object Detection is a prominent area in computer vision, where deep learning has dramatically advanced in many areas-from autonomous driving and healthcare to surveillance. Discuss the development of deep learning models for object detection: two-stage detectors like Faster R-CNN, one-stage detectors as YOLO and SSD, and emerging transformer-based models like DETR. We discuss strengths and weaknesses of each type of model with respect to accuracy, speed, and efficiency of resources used, specifically looking at the challenges such models pose in real applications like occlusion, detection of small objects, and domain adaptation. Finally, we describe how large datasets like MS COCO and PASCAL VOC became important to the development of benchmarks. Future promising research directions would be multi-modal learning, lightweight models for resource-constrained devices, and ethics considerations for privacy-sensitive applications. This review tries to outline the state-of-the-art object detection methodology available nowadays, indicates the challenges of the present situation, and points out how further development might occur. Keywords: Computer Vision, Deep Learning, R-CNN, YOLO, SSD, DETR, MS COCO, PASCAL VOC, Multi-modal learning.
Copyright
Copyright © 2025 Dr Neha yadav. This is an open access article distributed under the Creative Commons Attribution License.
