This paper provides a comprehensive review of deep learning-based object detection frameworks, highlighting the evolution from traditional methods to more advanced deep neural networks. Traditional object detection methods, which rely on handcrafted features and shallow architectures, often struggle with performance due to their inability to handle complex ensembles of low-level and high-level features. The advent of deep learning has introduced more powerful tools that can learn semantic, high-level, and deeper features, leading to significant improvements in object detection performance. The review begins with an introduction to deep learning and Convolutional Neural Networks (CNNs), followed by a detailed discussion of generic object detection architectures. These architectures are categorized into region proposal-based and regression/classification-based frameworks. Region proposal-based methods, such as R-CNN, SPP-net, Fast R-CNN, Faster R-CNN, R-FCN, FPN, and Mask R-CNN, involve generating region proposals, extracting features, and performing classification and localization. Regression/classification-based methods, including YOLO and SSD, directly map image pixels to bounding box coordinates and class probabilities, offering real-time performance. The paper also explores specific tasks such as salient object detection, face detection, and pedestrian detection, detailing their unique characteristics and challenges. Experimental analyses are provided to compare various methods, and several promising future directions are outlined to guide future research in object detection and related neural network-based learning systems.This paper provides a comprehensive review of deep learning-based object detection frameworks, highlighting the evolution from traditional methods to more advanced deep neural networks. Traditional object detection methods, which rely on handcrafted features and shallow architectures, often struggle with performance due to their inability to handle complex ensembles of low-level and high-level features. The advent of deep learning has introduced more powerful tools that can learn semantic, high-level, and deeper features, leading to significant improvements in object detection performance. The review begins with an introduction to deep learning and Convolutional Neural Networks (CNNs), followed by a detailed discussion of generic object detection architectures. These architectures are categorized into region proposal-based and regression/classification-based frameworks. Region proposal-based methods, such as R-CNN, SPP-net, Fast R-CNN, Faster R-CNN, R-FCN, FPN, and Mask R-CNN, involve generating region proposals, extracting features, and performing classification and localization. Regression/classification-based methods, including YOLO and SSD, directly map image pixels to bounding box coordinates and class probabilities, offering real-time performance. The paper also explores specific tasks such as salient object detection, face detection, and pedestrian detection, detailing their unique characteristics and challenges. Experimental analyses are provided to compare various methods, and several promising future directions are outlined to guide future research in object detection and related neural network-based learning systems.