This paper provides a comprehensive review of deep learning-based object detection frameworks. Object detection involves identifying and locating objects in images, which is crucial for computer vision applications. Traditional methods rely on handcrafted features and shallow architectures, but deep learning has enabled more powerful models that can learn semantic, high-level features. The paper discusses the evolution of deep learning, the role of Convolutional Neural Networks (CNNs), and various object detection architectures. It covers generic object detection, salient object detection, face detection, and pedestrian detection, highlighting their differences and applications. The paper also discusses the challenges in object detection, such as varying viewpoints, poses, and lighting conditions, and how deep learning models address these issues. It reviews several state-of-the-art models, including R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, and discusses their performance and efficiency. The paper also explores future directions in object detection, including multi-task learning, multi-scale representation, and contextual modeling. Overall, the paper emphasizes the importance of deep learning in advancing object detection techniques and provides insights into the current state and future potential of the field.This paper provides a comprehensive review of deep learning-based object detection frameworks. Object detection involves identifying and locating objects in images, which is crucial for computer vision applications. Traditional methods rely on handcrafted features and shallow architectures, but deep learning has enabled more powerful models that can learn semantic, high-level features. The paper discusses the evolution of deep learning, the role of Convolutional Neural Networks (CNNs), and various object detection architectures. It covers generic object detection, salient object detection, face detection, and pedestrian detection, highlighting their differences and applications. The paper also discusses the challenges in object detection, such as varying viewpoints, poses, and lighting conditions, and how deep learning models address these issues. It reviews several state-of-the-art models, including R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, and discusses their performance and efficiency. The paper also explores future directions in object detection, including multi-task learning, multi-scale representation, and contextual modeling. Overall, the paper emphasizes the importance of deep learning in advancing object detection techniques and provides insights into the current state and future potential of the field.